social media addiction research proposal

To read this content please select one of the options below:

Please note you do not have access to teaching notes, influencing factors of social media addiction: a systematic review.

Aslib Journal of Information Management

ISSN : 2050-3806

Article publication date: 5 September 2023

As an unhealthy dependence on social media platforms, social media addiction (SMA) has become increasingly commonplace in the digital era. The purpose of this paper is to provide a general overview of SMA research and develop a theoretical model that explains how different types of factors contribute to SMA.

Design/methodology/approach

Considering the nascent nature of this research area, this study conducted a systematic review to synthesize the burgeoning literature examining influencing factors of SMA. Based on a comprehensive literature search and screening process, 84 articles were included in the final sample.

Analyses showed that antecedents of SMA can be classified into three conceptual levels: individual, environmental and platform. The authors further proposed a theoretical framework to explain the underlying mechanisms behind the relationships amongst different types of variables.

Originality/value

The contributions of this review are two-fold. First, it used a systematic and rigorous approach to summarize the empirical landscape of SMA research, providing theoretical insights and future research directions in this area. Second, the findings could help social media service providers and health professionals propose relevant intervention strategies to mitigate SMA.

• Social media addiction
• Influencing factors
• Literature review
• Theoretical framework
• Addiction mechanism
• Stimulus-organism-response framework

Acknowledgements

The authors are grateful to the editor and reviewers whose constructive comments have improved the quality of this manuscript considerably. This research was supported and funded by the following grants: National Social Science Foundation of China (21&ZD334) and the Science Fund for Creative Research Groups of NSFC (71921002).

Liang, M. , Duan, Q. , Liu, J. , Wang, X. and Zheng, H. (2023), "Influencing factors of social media addiction: a systematic review", Aslib Journal of Information Management , Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/AJIM-10-2022-0476

Emerald Publishing Limited

Copyright © 2023, Emerald Publishing Limited

Related articles

All feedback is valuable.

Please share your general feedback

Report an issue or find answers to frequently asked questions

Contact Customer Support

• Open access
• Published: 13 July 2023

Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults

• Deon Tullett-Prado 1 ,
• Jo R. Doley 1 ,
• Daniel Zarate 2 ,
• Rapson Gomez 3 &
• Vasileios Stavropoulos 2 , 4  

BMC Psychiatry volume  23 , Article number:  509 ( 2023 ) Cite this article

7524 Accesses

11 Altmetric

Metrics details

Problematic social media use has been identified as negatively impacting psychological and everyday functioning and has been identified as a possible behavioural addiction (social media addiction; SMA). Whether SMA can be classified as a distinct behavioural addiction has been debated within the literature, with some regarding SMA as a premature pathologisation of ordinary social media use behaviour and suggesting there is little evidence for its use as a category of clinical concern. This study aimed to understand the relationship between proposed symptoms of SMA and psychological distress and examine these over time in a longitudinal network analysis, in order better understand whether SMA warrants classification as a unique pathology unique from general distress.

N  = 462 adults ( M age  = 30.8, SD age  = 9.23, 69.3% males, 29% females, 1.9% other sex or gender) completed measures of social media addiction (Bergen Social Media Addiction Scale), and psychological distress (DASS-21) at two time points, twelve months apart. Data were analysed using network analysis (NA) to explore SMA symptoms and psychological distress. Specifically, NA allows to assess the ‘influence’ and pathways of influence of each symptom in the network both cross-sectionally at each time point, as well as over time.

SMA symptoms were found to be stable cross-sectionally over time, and were associated with, yet distinct, from, depression, anxiety and stress. The most central symptoms within the network were tolerance and mood-modification in terms of expected influence and closeness respectively. Depression symptoms appeared to have less of a formative effect on SMA symptoms than anxiety and stress.

Conclusions

Our findings support the conceptualisation of SMA as a distinct construct occurring based on an underpinning network cluster of behaviours and a distinct association between SMA symptoms and distress. Further replications of these findings, however, are needed to strengthen the evidence for SMA as a unique behavioural addiction.

Peer Review reports

Introduction

In recent years, increased attention has been paid to phenomena of excessive social media use, impacting users’ lives in a way not dissimilar to substance addiction [ 1 ]. When in this state, known as ‘Problematic Social Media Use (PSMU), one’s social media usage occupies their daily life, to the extent that their other roles and obligations maybe compromised (e.g., family, romance, employment; [ 1 , 2 ]. In that line, PSMU impact has been demonstrated by its significant associations with mood disorder symptoms, low self-esteem, disrupted sleep, reduced physical health and social impairment [ 3 , 4 ]. Given that PSMU prevalence has been estimated to vary globally between 5%-10% of the social media users’ population [ 1 , 5 , 6 ], which exceeds 80% among more developed countries, such as Australia, and has the prospective to rise [ 7 , 8 ], PSMU related mental health concerns present compelling. Despite these, a rather disproportional paucity of longitudinal research regarding the nature, causes and treatment of PSMU has been repeatedly illustrated [ 1 , 9 ]. Attending such remarks, the present study aspires to examine the structure of PSMU’s most popular conceptualisation (as inspired by the behavioural addiction model [ 2 ]), whilst concurrently assessing its relationship with depression/distress behaviours via adopting and innovative network approach.

Conceptualizing problematic social media use

When attempting to conceptualise PSMU, the most employed definitions involve the so called “behavioural addiction model” [ 1 , 9 ]. Labelled as ‘Social Media Addiction’ (SMA), this conceptualization of PSMU is characterized by a deep fixation/drive towards the use of social media that has become uncontrollable and unhealthy. This model features a number of addiction symptoms drawn from those experienced by substance and gambling addicts, with six symptoms derived from Griffiths key-components of addiction [ 10 , 11 ]. These symptoms entail salience (i.e., preoccupation with social media usage), mood modification (i.e. using Social Media to alleviate negative moods/states), tolerance (i.e. requiring more social media engagement over a period of time in order to attain the same degree of satisfaction/mood modification), withdrawal (i.e. the experience of discomfort/distress/irritability/frustration, when attempting to cease/reduce use), relapse (i.e. failed attempts to control social media usage) and conflict/social impairment (i.e. social media use interferes with, and damages, one’s social life, emotional wellbeing, educational attainment, career and/or other activities/needs; [ 12 ]).

A number of separate theories have also been put forwards, such as models describing Problematic Social Media Use in terms of dysfunctional motivations or contexts for use [ 13 , 14 ]. Similarly, various instruments have been developed to reflect conceptual variability when assessing PSMU (e.g., Social Media Disorder Scale [ 15 ]; Bergen Social Media Addiction Scale [ 11 ]). However, the SMA model, as characterized by Griffiths 6 core components of addiction has seen the most use and acceptance, with a number of studies having evidenced the manifestation of those symptoms (e.g., tolerance, relapse, conflicts [ 11 , 16 ], identified motivations and risk factors similar to addiction (e.g., brain/neurological similarities between substance and SMA addicts [ 13 , 14 , 17 ]) and developed measurement tools based on this model [ 9 , 11 , 15 , 18 ]. Based on the above, the six symptom SMA model of PSMU, as measured via the Bergen Social Media Addiction Scale (BSMAS [ 11 ]) is employed going forward in this study.

Despite this level of acceptance, this “addiction” like definition of PSMU/SMA remains the object of controversy [ 19 ]. Criticisms abound regarding the model, with some labelling it a premature pathologizing of ordinary social media use behaviours with low construct validity and little evidence for its existence [ 19 , 20 ]. For example, Huang [ 21 ] highlight positive associations between social media and physical activity, denoting that not all social media use would necessarily represent a problematic behavior. Nonetheless, the lack of clarity surrounding the links between excessive social media use symptoms and markers of impairment, such as distress has been pointed out as cause for caution [ 19 ]. For instance, it has been argued that while preoccupation behaviours may be harmful when involving substances, they don’t necessarily carry the same weight in a behavioural addiction such as SMA [ 22 ]. In addition, it is argued that links between SMA and more well recognised disorders, such as Depression, may imply that SMA is in fact a secondary symptom of pre-existing depression, and not a distinct condition itself [ 19 ]. Given that research in this area is still highly exploratory these criticisms are difficult to dispel [ 9 ]. Thus, there is a need for research clarifying the nature of SMA, its longitudinal effects, and the relative importance of each SMA proposed symptom, as well as ways in which symptoms associate risk factors/negative outcomes.

SMA and longitudinal network analysis

One avenue of addressing this need could be offered via the implementation of longitudinal network analysis [ 23 ]. Network analysis is an exploratory approach of assessing constructs, as mirroring networks of symptoms/behaviours, where a number of variables/behaviours are examined together, whilst information is simultaneously collected regarding their inter-relationships and relative influence, so as to create a graphical ‘network’ (i.e., visualization of the construct’s underpinning behaviours; [ 23 , 24 , 25 ]). This analysis allows one to examine a set of symptoms from an utterly different viewpoint than traditional latent-variable perspectives. Rather than viewing symptoms as resulting from the presence of a latent construct (SMA for example), network analysis assumes symptoms are formative. Which is to say, as causes in themselves, interacting with each other and with other risk factors/negative outcomes to compose/form the “disorder” [ 24 ]. This allows the unique relationships, known as “edges”, between all considered variables/behaviours/manifestations, called “nodes”, to be observed, in a capacity not available with traditional structural equation modelling (SEM [ 26 ]). For example, examination of the so called symptom “centrality” (i.e. relative influence of each distinct symptom on other symptoms/behaviours included in an examined network), instead of symptom severity, may enable the detection of symptoms/behaviours with the largest influence on others, and thus contribute in evaluating: a) their “central” (or more peripheral role) in defining a proposed disorder (e.g. SMA), and; b) their targeted priority in a potential intervention program [ 27 ]. This can be done in great detail with separate centrality indices providing an indication of: a) the summed associations between a symptom/behaviour and all others examined (i.e., strength; Expected Influence in the case of psychopathology); b) the degree to which a symptom serves as an intermediary between others (i.e. betweenness) and; c) how closely a symptom aligns with others (i.e., closeness [ 28 ]). Furthermore, similar centrality relationships between distinct clusters of symptoms can be examined, with the so called “bridge” (i.e. a point that connects two distinct groups of behaviours) centrality indices (i.e. bridge strength; bridge expected influence; bridge betweenness and closeness) providing indications of which symptoms bind distinct disorders, such as SMA and depression together, either serving as intermediaries between disorders and/or by being more proximal to other disorders [ 28 ].

Such detailed examination of the relationships between symptoms, and clusters of symptoms, can further serve to test the veracity of models and constructs, which is particularly important for solidifying the occurrence of SMA [ 19 ]. For example, if the symptoms/behaviours informing a model, don’t relate at all, or accumulate into tight, separate ‘clusters’, then the construct may not be valid [ 29 ]. Additionally, with testing identical construct networks across two or more timepoints, the over-time stability of a proposed network can be examined, further validating a given construct (i.e., if the SMA symptoms’ network remains stable over time, then the construct is likely experienced longitudinally similarly [ 30 ]).

Aside of considering the stability of a network over time, network analysis procedures enable attaining stability coefficients for the edge weights and centrality indicators irrespective of the population/data examined via the use of case-dropping bootstrapping to examine the potential variance in these indices (i.e. network analysis indices such as strength and/or expected influence are re-estimated based on various alternative compositions/ re-samples of the data considered [ 31 , 32 ]. Unstable indices, either population-wise or over time are invalid, and their use is generally dismissed [ 33 ]. Finally, network analysis gives one the opportunity to evaluate not only the relationships of behaviours being considered as composing a single disorder, but also to examine how these distinct disorder informing symptoms/behaviours may interact with other separate comorbid disorders (i.e. in this case SMA behaviours and depression/ anxiety [ 31 ]). This allows the examination of how these variables formatively interact with one another, as well as indicating their separate/distinct concurrent validity [ 34 ].

Indeed, the need of securing such information regarding the distinct proposed SMA symptoms and their associations with comorbid depression and/or distress behaviours experienced is reinforced by recent item response theory (IRT) and network analysis findings of responses on the Bergen Social Media Addiction Scale [ 35 , 36 ]. Stănculescu [ 35 ] identified SMA behaviours of “salience” and “withdrawal” as having the highest centrality, whilst SMA “relapse” behaviours as having the lowest centrality, in the context of the 6 SMA symptoms consisting of a single unitary cluster with strong inter-relations. However, these findings despite constituting an important step, present limited in a number of ways. Firstly, they are derived from a Romanian sample ( N  = 705), where specific cultural characteristics may apply, restricting their generalizability to different populations. Secondly, due to being cross-sectional they don’t allow the examination of the stability of the network associations over-time [ 29 , 31 , 32 ]. Thirdly, Stănculescu’s [ 35 ] examination of the SMA symptom network only took expected influence into account considering centrality and did not consider the significance of differences in the centrality of nodes. Finally, the network examined by Stănculescu [ 35 ] involved no covariates aside of the 6 SMA symptoms. Thus, the extent of differentiation of various SMA behaviours/criteria from comorbid conditions and/or their specific associations with other commonly proposed SMA risk factors and negative outcomes (e.g. depression, anxiety) could not be established [ 37 ]. To contribute to the available knowledge in the field, the present study aims to use network analysis modelling to longitudinally examine SMA symptoms in conjunction with commonly proposed comorbid excessive digital media usage conditions involving experiencing distress (i.e., depression and anxiety [ 37 , 38 , 39 ]).

Distress and SMA

Psychological distress is defined as a state of psychological suffering characterized by anxiety, depression and stress, and often serves as a general measure of mental health [ 37 , 40 ]. In this capacity, investigating the ways in which SMA and distress behaviours interact, can potentially produce a clearer understanding for how a person’s mental health could be distinctly affected by the separate symptoms of SMA and/or the vice versa (e.g., Is it SMA related preoccupation, tolerance and/or withdrawal more related to anxiety and/or depression experiences?). As distress involves some of the most well researched comorbidities of SMA (e.g., depression, anxiety), there is a wealth of prior research indicating the presence of distress-SMA interactions [ 41 , 42 ]. For instance, different aspects of social media use, such as the purpose of using social media (e.g., adaptive/maladaptive coping mechanisms [ 43 ]), their preferred social media activities, as well as behaviours of excessive social media usage have been consistently associated with an individual’s proneness/risk for depression, anxiety and stress [ 41 , 42 ]. Such links tend to be more evident in younger populations, where social media use often drives/underpins psychological distress for a proportion of users (e. g. a developing individual might feel distressed for deviating from what is presented as ideal or common by their peers online [ 44 ]). A wide variety of explanations have been put forth as potential reasons for such distress-SMA links involving: a) distressed individuals excessively utilizing social media use as a way to cope; b) the deleterious effects excessive social media use has on sleep, time management, physical activity, the development of social skills and; c) the near constant access social media provides to information of others, prompting comparisons and negative social interactions [ 42 ]. However, these, independent findings present as fragmented, the clinically relevant, over-time links/associations between specific SMA symptoms and the levels of depression, anxiety and stress one experiences remaining unclear. Such clinically important knowledge can be offered by longitudinal network analysis, which has not been yet, to the best of the authors’ knowledge, attempted concerning these variables.

The findings of such an analysis are envisaged to also have significant epidemiological utility. Given the acknowledged connection between psychological distress and SMA behaviours [ 41 , 42 ], and the noted drive of psychologically distressed individuals towards coping strategies involving escapism via social media facilitated pleasurable activities [ 44 ], it is possible-and indeed argued by some-that PSMU may not in fact represent an addiction (the SMA model) but simply be a secondary symptom of distress [ 19 ]. By examining the SMA model in conjunction with symptoms of distress, the connections between the SMA symptoms and Distress symptoms can be demystified with detail, their bridges can be identified, whilst deeper insight may be gleaned into the relationship between Distress and SMA.

The present study

Prompted by the above literature, the present study aimed to contribute to the field via innovatively, longitudinally, examining a normative, community sample of social media users, assessed across two time points, one year apart, regarding both their SMA and distress behaviours. Specifically, it assessed their responses via advanced longitudinal network analysis’ modelling, enhanced by the use of machine learning algorithms to increase knowledge regarding: a) the validity/sufficiency of the widely popular SMA conceptualization; b) persistent differential diagnosis considerations regarding SMA and distress conditions entailing depression, anxiety and stress and; c) pivotal/central behaviours considering SMA manifestations over time. Thus, the following three aims were devised: 1) To reveal/describe the network structure of the six SMA symptoms and symptoms of depression, anxiety and stress; 2) To examine potential clustering in this revealed SMA-distress network, as well as to identify any specific bridges or routes between the clusters in this network, and; 3) To examine the stability of the revealed SMA-distress network over time and across different potential sample compositions.

Participants

An online sample of adult, English speaking participants aged 18 to 64 who were familiar with social media [ N  = 462, M age  = 30.8, SD age  = 9.23, n males  = 320 (69.3%), n females  = 134, (29%), n other  = 9, (1.9%); 968 complete responses wave 1- 506 attrition between waves = 462] was assessed across two time points, 12 months apart. Acknowledging that adequate sample size rules of thumb are still explored for longitudinal network analysis [ 45 ], the current sample size well exceeds the threshold of 350 recommended for sparse networks up to 20 nodes in order to accurately estimate moderate sensitivity, high specificity and likely high edge weights correlations [ 46 ]. Furthermore, the 53.27% attrition ( N  = 506) between the two waves of data collection was studied. Specifically, attrition/retention was inserted as an independent dummy coded variable (i.e. 1 = attrition, 0 = retention between wave 1 and wave 2) to assess its associations with sociodemographic characteristics of the sample (via crosstabulation, X 2 ), as well with SMA, depression, anxiety and stress rates (via t test). There were no significant associations between social media scores at time-point 1 and 2 ( Welch’s t [953]  = 1.60, p  = 0.11, Cohen’s d  = 0.10). Moreover, older straight males showed decreased attrition rates (Age: Welch’s t [960]  = -4.05, p  < 0.01, Cohen’s d  = -0.26; Gender: χ 2 [2] = 12.4, p  < 0.01, Cramer’s V  = 0.11); however, all differences represented a small effect size. In terms of sociodemographic, variations were observed, with very significant amounts of our sample heralding from diverse backgrounds. For example, 38.1% of the sample heralded from non-white backgrounds and 30.5% of the sample was female or nonbinary. See Table 1 for the sociodemographic information of those addressing both waves and included in the current analyses.

Aside of collecting socio-demographic information the following instruments were employed for the current study:

Bergen Social Media Addiction Scale (BSMAS; [ 11 ] )

The BSMAS measures the severity of one’s experience of the six proposed SMA symptoms via an equivalent number of items that ask to which degree certain behaviours associated with these symptoms relate to one’s own life (i.e., salience, tolerance, mood modification, relapse, withdrawal and conflict [ 11 ]). The items of the BSMAS include “ You spend a lot of time thinking about social media or planning how to use it ” (salience), “You feel an urge to use social media more and more” (tolerance), “You use social media in order to forget about personal problems” (mood modification), “You have tried to cut down on the use of social media without success” (Relapse), “You become restless or troubled if you are prohibited from using social media” (withdrawal) and “You use social media so much that it has had a negative impact on your job/studies” [ 11 ]. These items are rated on a 5-point scale scored from 1 (very rarely) to 5 (very often), with higher scores indicating a greater experience of SMA Symptoms [ 11 ]. A total score ranging between 6 and 30 is comprised by the accumulation of the different items’ points reflecting overall SMA behaviors. Considering the current sample, Cronbach’s α and the McDonalds ω internal reliability indices were both 0.88 for time point one and increased to 0.90 for time point two.

Depression, Anxiety and Stress Scales-1 (DASS-21; [ 47 ] )

The DASS measures distress experiences and comprises 21 items, subdivided into three equal subscales (7 items each) addressing depression, anxiety and stress respectively [ 47 ]. Items examine distress behaviors with a 4-point likert-type scale ranging from 0 (did not apply) to 3 (applied most of the time). Total scores for each dimension are derived by the accumulation of the relevant items’ points ranging between 0–21 for the three factors. Considering time point 1, the Cronbach’s α indices for the subscales of depression, anxiety and stress were 0.94, 0.85 and 0.88 respectively and their corresponding McDonalds ω reliabilities were 0.94, 0.86 and 0.88. For time point 2, the same Cronbach α reliabilities were 0.93, 0.85 and 0.86 and their McDonalds ω reliabilities were 0.93, 0.86 and 0.86.

Approval was received from the Victoria University Human Research Ethics Committee (HRE20-169) and data for both time points was collected between 2020 and 2022. Time point 1 data ( N t1  = 968) was collected via an online survey link distributed via social media (e. g. Facebook; Instagram; Twitter), digital forums (e.g., reddit) and the Victoria University learning management system. The link first took potential participants to the Plain Language Information Statement (PLIS), which informed about the study requirements, responses’ anonymity and free of penalty withdrawal rights. After completing this step, eligible participants were asked to voluntarily provide their email address to be included in prospective data collection wave(s), and to digitally sign the study consent form (box ticking). Twelve months later (between August 2021 and August 2022), follow up emails involving an identical survey link (i.e., PLIS, email provision for the second wave, consent form and survey questions) were sent out for those interested to participate in the second data collection wave ( N t2  = 462). Participation in this study was voluntary.

Statistical analyses

A network model involving the six BSMAS symptoms and three DASS subscales was estimated for the two timepoints using the qgraph and networktools R packages [ 32 , 48 ]. Network models involve the creation of a network nodes and edges, where nodes represent considered variables/observations and edges the relationships between them [ 49 ]. Stronger relationships/edges are represented by thicker, darker lines with the distance between variables/nodes indicating their relevance/association (closer = higher relevance) and the colour indicating the direction of the relationship (Blue = positive, red = negative). This is done in the present case via the use of zero order correlations (i.e., no control for the influence of any other variables) combined with a graphical Least Absolute Shrinkage and Selection Operator algorithm (g-lasso; [ 49 ]) employed to shrink partial correlations to zero. Practically, this reduces the chance of false positives (i.e., Type 1 error), providing more precise judgements about the relationships between variables, whilst concurrently pruning excessively weak links to simplify networks [ 50 ].

Cross-sectional network stability

Once network models are estimated across time points, their respective centrality, edge weights and bridge values are assessed [ 49 ]. Centrality measures used here involve: a) degree (i.e., the number of links/edges held by each node); b) betweenness (i.e. the number of times a node lies on the shortest path between other nodes); c) closeness (i.e. the ‘closeness’ of each node to all other nodes); d) eigenvector (i.e. node centrality based not the node’s connections and additionally the centrality of the nodes they are connected with)] and; d) the ‘expected influence’ of a node for the whole network [ 51 ]. The latter accounts for negative influences/edges, promotes the overall stability in the network, and it is recommended for psychopathological networks [ 29 ]. Finally, bridge values represent the rate of nodes serving as connections between distinct network clusters and are measured via bridge expected influence indices [ 48 ].

The prerequisite for estimating these values is calculating their stability coefficients across time points. These denote the estimated maximum number of cases that can be dropped from the data to retain, with 95% probability, a correlation of at least 0.7 (default) between original network indices and those computed with less cases with an acceptable minimum probability of > 0.25 and preferably > 0.5 [ 32 ]. These were calculated using a modified version of the bootnet package with an end coefficient representing the proportion of the original sample that can be dropped before the centrality, bridge and edge weight values vary significantly [ 32 ].

Cross sectional network characteristics

Once network stability is confirmed, the networktools package estimates the centrality, edge weight and bridge indices and graphs the network. Judgements regarding differences in centrality across nodes or in the strength of edges are made using the centrality/edge difference tests via the bootnet R package [ 32 ]. These construct a confidence interval between the two regarded results, adjusted so that the lower the stability the greater the interval, with the difference deemed non-significant if the points are within it.

Stability of the network across time

To compare network stability across time points, the NetworkComparisonTest package is employed to specifically estimate their variance in terms of the global network structure, the global strength of the nodes, edges and centrality. Each of these tests is carried out in succession, with the latter two tests only being conducted by the package if the first two detected significant differences (i.e., if the networks across the two time points do not differ significantly, there is no point examining differences in more specificity; [ 52 ]). P -values less than 0.05 for these tests indicate significant differences.

Network generation and stability

Network Analyses generated two networks, one for each timepoint, depicted in Figs. 1 and 2 . Edge strengths and calculated centrality statistics for time point 1 are featured in Tables 2 and 3 , and for time point 2 in Tables 4 and 5 . Note that within the following figures, the BSMAS symptoms of salience, tolerance, mood modification, relapse, withdrawal and conflict are referred to as BSMAS_1, BSMAS_2, BSMAS_3, BSMAS_4, BSMAS_5 and BSMAS_6 respectively.

Network of the BSMAS symptoms and DASS subscales at time point 1

Network of the BSMAS symptoms and DASS subscales at time point 2

The network at time point one showed excellent stability in terms of its basic structure (edge stability coefficient = 0.75, expected influence centrality stability coefficient = 0.60) and marginal stability regarding secondary measures of centrality (closeness centrality stability coefficient = 0.13, betweenness centrality stability coefficient = 0.05). In terms of bridges between network clusters, stability ranged from acceptable (bridge expected influence stability coefficient = 0.36), to marginal (bridge betweenness stability coefficient = 0.0) to insufficient (bridge closeness stability coefficient = 0.0).

These structural network characteristics were shared with the network at time point two both in terms of basic structure (edge stability coefficient = 0.75, expected influence centrality stability coefficient = 0.60) and secondary measures of centrality (closeness centrality stability coefficient = 0.13, betweenness centrality stability coefficient = 0.05). Though the bridges between clusters featured greater stability than time point 1 (bridge expected influence stability coefficient = 0.52, bridge betweenness = 0.05, bridge closeness = 0.21).

With all necessary structural measure’s stability within acceptable limits, further analysis of the network structures and network comparison was undertaken. However, given the marginal to unacceptable stability of both closeness and betweenness as measures of centrality, it was deemed that results from these measures cannot be safely generalised, or safely used to draw inferences about the data. Thus, these measures are only considered in the following as potential indicators that may point to avenues of further investigation, unless a result of 0.0 was scored on their stability coefficient, in which case they are completely disregarded.

Network characteristics at Time Point 1

Figure  3 depicts the expected influence of all nodes at time point 1, and Fig.  4 depicts centrality difference tests determining the significance of differences in expected influence between all nodes, with black squares indicating significant differences. In terms of overall centrality, stress had the most and strongest connections with other nodes. Stress had expected influence significantly greater than the majority of nodes, with the exception of anxiety and the BSMAS symptoms of tolerance and mood modification (Items 2 & 3). These BSMAS symptoms formed a consistent plateau of centrality, significantly above the symptoms of Relapse and Withdrawal (Item 4 & 5 respectively). Depression was relatively low in centrality, with a result significantly lower than every other node except relapse and withdrawal.

Expected Influence across all nodes at time point 1

Centrality difference tests of Expected Influence at time point 1

Accordingly, Fig.  5 depicts nodes’ closeness and betweenness at time point 1, while Figs. 6 , 7 depict centrality difference tests determining the significance of differences in betweenness and closeness, with black squares indicating a significant difference. In terms of the number of times a node was on the shortest path (i.e., betweenness), there were no significant differences. In terms of the distance between nodes (i.e., closeness), BSMAS symptoms of mood modification and withdrawal displayed the greatest centrality, with each displaying significantly higher centrality in the network than the DASS subscales.

Closeness and betweenness across all nodes at time point 1

Centrality difference tests of betweenness at time point 1

Centrality difference tests of closeness at time point 1

Figure  8 depicts edge difference tests, indicating that the edges between anxiety and stress, depression and stress, and between the BSMAS symptoms of salience and tolerance were significantly stronger than those of other nodes.

Edges’ difference tests at time point 1

Bridge characteristics at Time Point 1

Figures  9 and 10 depict bridge expected influence, closeness and betweenness centralities between the BSMAS symptoms and the DASS subscales. SMA symptoms of mood modification and conflict demonstrated markedly higher expected influence connections with the DASS subscales cluster than other SMA symptoms. With regards to the DASS subscales, anxiety and stress were in a similar position, with a bridge expected influence on the BSMAS symptoms substantially greater than that of depression (see Fig.  9 ). In terms of the proximity/closeness between nodes in the two subgroups, the BSMAS symptom of mood modification (Item 3) and withdrawal (Item 5) were the most proximal to the distress subgroup, with depression serving as the closest connecting point.

Bridge Expected Influence Centrality at time point 1

Bridge Closeness Centrality at time point 1

Network characteristics at Time Point 2

Figure  11 depicts the expected influence of all nodes at time point 2, whilst Fig.  12 depicts the significance of nodes’ differences in terms of their expected influence. The highest overall centrality in terms of expected influence was demonstrated by the BSMAS symptom of tolerance (Item 2), which was closely followed by the DASS subscale of stress. As is evidenced in Fig.  12 , both stress and tolerance were significantly greater in their expected influence centrality than the other network nodes.

Expected Influence across all nodes at time point 2

Centrality difference tests of Expected Influence at time point 2

Figures  13 and 14 depict the betweenness and closeness respectively of all nodes at time point 2, whilst Figs. 15 and 16 depict centrality difference tests determining the significance of differences in betweenness and closeness respectively. No significant differences in the number of times a node was on the shortest path (i.e., betweenness) identified between the nodes, nor were there any nodes significantly higher in closeness, with the exception of withdrawal (Item 5).

Betweenness across all nodes at time point 2

Closeness across all nodes at time point 2

Centrality difference tests of betweenness at time point 2

Centrality difference tests of closeness at time point 2

Figure  17 depicts edge difference tests at time point 2. As with time point 1, the edges between anxiety and stress, depression and stress, and between the BSMAS symptoms of salience and tolerance (Items 1 & 2) were significantly stronger than those between other nodes. Additionally, the connection between the BSMAS symptoms of tolerance and mood modification (Items 2 & 3) was a significantly stronger connection than over half of those assessed.

Edges’ difference tests at time point 2

Bridge characteristics at Time Point 2

Figures  18 , 19 and 20 depict bridge centralities between the BSMAS symptoms cluster and the DASS subscales cluster at time point 2. As in time point 1, the SMA symptoms of mood modification (Item 3) and conflict (Item 6) bridged the SMA behaviours cluster to the DASS subscales cluster via the nodes of anxiety and stress. These results were displayed in both the number and strength of connections between these nodes (expected influence centrality) and the number of times these nodes were used as connecting joints in paths between other nodes in these two networks (betweenness centrality). Further, in terms of the proximal distance between nodes in the two subgroups, the BSMAS symptom of conflict was the most central symptom, with anxiety and stress being the most proximal distress experiences.

Bridge Expected Influence Centrality at time point 2

Bridge Closeness Centrality at time point 2

Bridge Betweenness Centrality at time point 2

Longitudinal network comparison

Finally, a network invariance test revealed no significant differences between the network at time point 1 and time point 2 in terms of global network invariance ( p  = 0.36) and global strength Invariance ( p  = 0.42).

The rapid expansion of social media use has generated concerns regarding the development of PSMU behaviours. These have been noted to closely resemble those displayed in substance/behavioural addictions [ 1 , 2 ]. In that line, a portion of scholars have defined these behaviour as social media addiction (SMA) and have advocated in favour of describing it via the lenses of the components model of addiction framework (i.e. salience; mood-modification; tolerance; relapse; withdrawal; losing of interest into other activities/functional impairment; [ 1 , 9 ]. Such suggestions have been criticised as accommodating the risk of pathologizing common everyday behaviours, such as the use of social media, and lacking validity due to adhering to substance abuse criteria/behaviours that may fail to correctly depict this emerging condition [ 19 , 20 ]. Additionally, there is a lack of clarity regarding the details of links between excessive use symptoms and markers of impairment, such as distress, which cause further doubts [ 19 , 20 ]. Finally, the occurrence of SMA behaviour as an independent diagnostic condition has been contested on the basis of SMA related behaviours constituting biproducts/ secondary symptoms of primarily distress conditions such as depression, anxiety and stress [ 19 , 20 ].

To address these concerns, the current research innovated via longitudinally assessing a normative cohort of adult social media users twice over a period of two years considering concurrently their SMA and depression, anxiety and stress self-reported experiences. Advanced longitudinal network analysis models, enriched via the LASSO algorithm, were calculated for both time points [ 29 , 32 ]. These aimed to firstly clarify whether SMA criteria, as described on the basis of the components model of addiction, formed indeed an underpinning network of behaviours, stable over time and across different sample compositions [ 10 ]. Answering this question would indicate that the construct is rather formative and not reflective (i.e., it is not just a conception of scholars or a sample specific construct, while it is steadily reflected the same way over time [ 19 , 20 ]).

Secondly, the analysis aimed to dispel to what extent SMA behaviours may mix/blend or closely relate to distress behaviours such as depression, anxiety and stress [ 53 ]. If the latter was to be true, then the SMA and distress components of the network would be expected to mix and not to represent distinctly different network clusters (i.e. SMA and distress related behaviours would represent different behavioural network clusters and thus should be classified independently). Thirdly, it was aimed to identify key/central/pivotal behaviours in the broader network, that should be prioritized in prevention and/or intervention for those presenting with SMA and/or comorbid depression, anxiety and stress (i.e. central nodes of the network with higher expected influence). Findings indicated that SMA behaviours/criteria, as per the components model of addiction, do constitute a formative network of symptoms, which is not sample or time specific. Furthermore, the SMA behaviours cluster was distinct to that of depression, anxiety and stress experiences across both measurements, favouring its classification as an independent diagnostic condition. Lastly, mood modification appeared to be consistently (across both time points) a central network node and has been facilitating as the main bridge primarily with distress symptoms of stress and anxiety rather than depression.

SMA and distress network

As summarized prior, results portrayed a stable overtime network cluster of SMA symptoms, which is associated yet distinct, to the distress related cluster of nodes composed by depression, anxiety and stress. These findings appear to align with the recent SMA, cross-sectional, network analysis study of Romanian data, which also supported the SMA defined behaviours of salience, tolerance, mood-modification, withdrawal, relapse and functional impairment being closely related and informing a clear cluster of nodes [ 35 ]. Therefore, the present study argues in favour of the idea of SMA operating as a formative construct, which occurs independently of the conception of scholars (i.e. does not only reflect theoretical conceptualizations [ 19 , 20 ]. This provides an indication in favour of those who support the SMA conceptualization and potentially the introduction of a distinct diagnostic category to capture the syndrome [ 35 , 36 ]. In that context, SMA behaviours related to mood-modification appeared to be central across both time points, reinforcing the idea of addictions, such as SMA, acting the problematic solution (e.g., way to either experience more positive or buffer negative emotions) of the distress generated by other problems [ 53 ]. Nevertheless, one cannot exclude the need of additional nodes, such as those likely reflecting “deception behaviours associated to the use of social media” (e.g. an individual concealing the amount of time they consume on social media usage) and/or relationship difficulties (e.g. as with other forms of addictions, a person may be marginalized within their social surrounding) to better describe the phenomenon [ 54 ]. Thus, although findings support the six, adjusted to the abuse of social media, addiction criteria operating as a distinct, SMA underpinning, formative network, the need for additional behavioural nodes to better describe the condition cannot be excluded.

Despite these, and in contrast to the results of the Stănculescu [ 35 ] Romanian study, where salience and withdrawal were identified as the most ‘central’ symptoms, the current study identified tolerance and mood-modification as the most highly central in terms of expected influence and closeness respectively. A possible explanation for this discrepancy may refer to the more rigorous methodology and wider aims applied in the current study, compared to that conducted by Stănculescu [ 35 ]. Firstly, the current analysis examined network stability across different resamples (i.e., potential population compositions) and over time (i.e. longitudinally), which was not the case in the Stănculescu [ 35 ] study. Secondly, the present study thoroughly examined centrality differences based on t-test comparisons in conjunction with the visual graph/network inspection, whilst such comparisons were not reported in the Romanian study [ 35 ]. Thirdly, centrality indices informing the present findings were referring to the extended network of SMA and distress behaviours, and not the narrower network of SMA behaviours only [ 35 ]. Thus, it is likely that whilst salience and withdrawal may be more central in the context of SMA behaviours, without taking into consideration concurrent depression, anxiety and stress behaviours; tolerance and mood modification maybe more pivotal in the broader context of SMA and distress comorbidities together. Finally, it is also likely that cultural differences between the two samples may alternate the experience of SMA between the populations, such that withdrawal and salience maybe more central for the Romanian sample [ 35 ]. Such differences inevitably invite further investigation regarding the cross-cultural invariance of the SMA network, as with other behavioural addictions related to the abuse of digital media (see gaming disorder [ 53 , 54 ]).

The current findings were also revealing considering the differential diagnosis concerns referring to SMA behaviours constituting primarily a secondary symptom of distress behaviours related to depression, anxiety and stress, rather than a distinct condition itself [ 54 ]. Specifically, network models across both time points consistently revealed two distinguishable clusters of nodes within the broader network, clearly dividing SMA and distress behaviours. Thus, although distress and SMA behaviours appeared related, they were not blended/mixed in a way that would advocate a common classification [ 41 ].

Furthermore, the current study also expands available knowledge regarding the relationship between SMA and distress, via the examination of the ‘bridging centrality’ of the various symptoms [ 54 ]. Primarily, the connections between the SMA behaviours of mood-modification and conflict, with anxiety and stress, appear to have acted as comorbidity bridges, featuring the highest expected influence bridge centrality values amongst their respective subnetworks (i.e., the number and strength of connections to other subnetworks). In addition, withdrawal symptoms served as a “go-between” in this link between subnetworks, with the highest betweenness bridge centrality (the amount of and strength of the connections between SMA and distress that used it as a go-between). Thus, these findings imply that the need to moderate one’s negative feelings via SMA, and/or the stress/anxiety related to the occurrence of functional impairments in a person’s life (e.g., conflicts with others due to SMA behaviours) could operate as the main connection points in the cyclical relationship between distress and SMA. This hypothesized process aligns with evidence relevant to other behavioural addictions [ 55 ]. Thus, one could support that stressed and anxious individuals may excessively use social media to cope with, and to modify their anxious manifestations, suffering conflicts with their real-world obligations and desires as a result of that use. The latter might induce more stress and anxiety, and perhaps even more when withdrawals ensue after failed attempts to reduce use. Further SMA and depression symptoms could follow as a result of the development of conflict/mood-modification and stress/anxiety respectively. This interpretation is reinforced by prior cross-sectional and longitudinal research in the field of addiction psychology that: a) portrays stress, as well as unhealthy coping mechanisms in response to stress, to operate as primary causes of addictions [ 56 , 57 , 58 , 59 ] and; b) proposes the need to escape from negative moods as highly associated to addictive tendencies [ 6 ]. These results may thus imply, that clinicians treating clients with comorbid SMA/distress, may wish to target these bridging symptoms in particular, in order to cut any possible bidirectional feedback loops between these disorders.

On a separate note, the depression node was found to display a seeming lack of importance in the network. Specifically, depressive behaviours were shown to possess significantly lower general centrality and bridge centrality, implying that they may not have as a formative effect on the experience of SMA symptoms, as stress and anxiety. Furthermore, depression displayed a negative association with withdrawal symptoms, the only negative association in the network. While initially this may seem to contradict prior research associating depression and social media use [ 41 ], this is not necessarily the case. Depression still displayed a positive association with the symptom of mood-modification, accommodating prior research linking addiction with the use of social media as a relief mechanism [ 6 ]. Furthermore, while at first it might seem oxymoronic that the experience of depression might associate with a reduction in SMA withdrawal symptoms, this may not be the case. It is likely that, as with other addictions, those experiencing depression are less able to attempt containing their addictive patterns, whilst when/if they do make attempts, those attempts may be less successful and thus they do not experience withdrawal [ 60 ]. Those experiencing depression have depressed mood, lack of energy and a lack of motivation all of which negate action and make it harder to quit or make an attempt to cease problematic behaviours [ 12 , 16 ]. Furthermore, a lack of direct impact of depressive experiences on SMA symptoms in the network does not imply a lack of impact overall. In the current findings, depression still displayed very strong relationships with stress and anxiety, allowing it to influence SMA via its influence on these symptoms. However, as causality associations were not directly explored in the current study, these interpretations require further additional evidence to be better supported.

Limitations and further study recommendations

Despite the relevant findings reported here, such conclusions and implications may need to be considered in the light of the several limitations of the present study. Firstly, a convenience, community, western/English speaking sample of adult social media users was collected, potentially restricting the generalization of the findings to non-western, children-adolescent and clinical populations. Secondly, findings were exclusively based on self-reported, psychometric scales and thus risks of subjectivity or self-reporting errors cannot be excluded. Therefore, considering that there is evidence of objectively measuring social media use [ 61 , 62 ] future researchers may wish to consider examining non-adult, non-western and/or clinical samples via multimethod designs entailing additionally physical actigraphy and/or digital monitoring means to further expand the available knowledge. Thirdly, this study focused exclusively on the network between PSMU and distress; however, other variables have been associated with PSMU and should be considered in future studies (e.g., fear of missing out [ 63 ]).

Conclusions and implications

Overall, the findings of the present study appear to have added important knowledge across three areas surrounding problematic social media usage. These involve the conceptualization of this debated condition, its differential diagnosis and key behavioural symptoms informing it [ 34 , 48 ]. In particular, the current findings support: a) the applicability of the SMA definition as a construct/condition naturally occurring based on an underpinning network cluster of behaviours; b) a distinct association between SMA symptoms and distress behaviours related to depression, anxiety and stress, which advocates the separate classification of SMA as a psychopathological condition and; c) the role of mood-modification drives and functional impairment/conflicts with others as the connecting/linking points with stress/anxiety behaviours in the formation of SMA behaviours. Accordingly, results pose three significant taxonomic, assessment and prevention/intervention implications. Firstly, the consideration of SMA as a distinct diagnostic category is strengthened. Secondly, assessment of comorbid stress and anxiety manifestations appears to require priority when addressing clients presenting with problematic social media usage. Thirdly, though individuals of different ages and sexes tend to use social media in different ways, and thus likely experience SMA in different fashions, the effects of age and sex on SMA symptoms and their relationship with distress was not explored. This represents an important and interesting area of future study that deserves to be examined.

Availability of data and materials

The data and materials used in this study are available in this link https://github.com/Vas08011980/SNSNETWORK/blob/main/html.Rmd

Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, Andreassen CS, Demetrovics Z. Problematic social media use: results from a large-scale nationally representative adolescent sample. PloS one. 2017;12(1):e0169839. https://doi.org/10.1371/journal.pone.0169839 .

Article   CAS   PubMed   PubMed Central   Google Scholar  

Griffiths, M. D., & Kuss, D. J. (2017). Adolescent social media addiction (revisited). Education and Health , 35 (3). https://irep.ntu.ac.uk/id/eprint/31776/1/PubSub9230_Griffiths.pdf

Anderson, E. L., Steen, E., & Stavropoulos, V. (2016). Internet use and problematic internet use: a systematic review of longitudinal research trends in adolescence and emergent adulthood. International Journal of Adolescence and Youth , 22 (4). https://doi.org/10.1080/02673843.2016.1227716

Gorwa R, Guilbeault D. Unpacking the social media bot: a typology to guide research and policy. Policy Internet. 2018;12(2):225–46. https://doi.org/10.1002/poi3.184 .

Article   Google Scholar  

Luo T, Qin L, Cheng L, Wang S, Zhu Z, Xu J, Chen H, Liu Q, Hu M, Tong J, Hao W. Determination the cut-off point for the Bergen social media addiction (BSMAS): Diagnostic contribution of the six criteria of the components model of addiction for social media disorder. J Behav Addict. 2021;10(2):281–90. https://doi.org/10.1556/2006.2021.00025 .

Article   PubMed   PubMed Central   Google Scholar  

Brailovskaia J, Schillack H, Margraf J. Tell me why are you using social media (SM)! Relationship between reasons for use of SM, SM flow, daily stress, depression, anxiety, and addictive SM use–An exploratory investigation of young adults in Germany. Computer and Human Behavior. 2020;113:106511. https://doi.org/10.1016/j.chb.2020.106511 .

DataReportal. (2021a). Digital 2021: Global overview report , (27 January 2021). Retrieved from https://datareportal.com/reports/digital-2021-global-overview-report . Accessed 25/11/2022.

Heffer T, Good M, Daly O, MacDonell E, Willoughby T, et al. The longitudinal association between social-media use and depressive symptoms among adolescents and young adults: an empirical reply to Twenge et al. (2018). Clin Psychol Sci. 2019;7(3):462–70. https://doi.org/10.1177/2167702618812727 .

Prinstein MJ, Nesi J, Telzer EH. Commentary: an updated agenda for the study of digital media use and adolescent development – future directions following Odgers & Jensen (2020). J Child Psychol Psychiatry. 2020;61(3):349–52. https://doi.org/10.1111/jcpp.13190 .

Article   PubMed   Google Scholar  

Griffiths, M. (2005). A ‘components’ model of addiction within a biopsychosocial framework. Journal of Substance Use, 10(4). https://doi.org/10.1080/14659890500114359

Andreassen CS, Torsheim T, Brunborg GS, Pallesen S. Development of a Facebook addiction scale. Psychol Rep. 2012;110(2):501–17. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517 .

Andreassen CS, Billieux J, Griffiths MD, Kuss D, Demetrovics Z, Mazzoni E, Pallesen S. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: a large-scale cross-sectional study. Psychol Addict Behav. 2016;30(2):252–62. https://doi.org/10.1037/adb0000160 .

Turel O, Serenko A. The benefits and dangers of enjoyment with social networking websites. Eur J Inf Syst. 2017;21(5):512–28. https://doi.org/10.1057/ejis.2012.1 .

Sun Y, Zhang Y. A review of theories and models applied in studies of social media addiction and implications for future research. Addictive Behavior. 2021;114:106699. https://doi.org/10.1016/j.addbeh.2020.106699 .

Van den Eijnden RJJM, Lemmens JS, Valkenburg PM. The social media scale. Computers in Human Behavior. 2016;61:478–87. https://doi.org/10.1016/j.chb.2016.03.038 .

Grau S, Keiser S, Bright L. Exploring social media addiction among student Millenials. J Cetacean Res Manag. 2019;22(2):200–16. https://doi.org/10.1108/QMR-02-2017-0058 .

He Q, Turel O, Brevers D, Bechara A. Excess social media use in normal populations is associated with amygdala-striatal but not with prefrontal morphology. Psychiatry Research: Neuroimaging. 2017;269:31–5. https://doi.org/10.1016/j.pscychresns.2017.09.003 .

Haand R, Shuwang Z. The relationship between social media and depression: a quantitative study among university students in Khost, Afghanistan. Int J Adolesc Youth. 2020;25(1):780–6. https://doi.org/10.1080/02673843.2020.1741407 .

Kardefelt-Winther D, Heeren A, Schimmenti A, van Rooij A, Maurage P, Carras M, Edman J, Blaszczynski A, Khazaal Y, Billieux J. How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction. 2017;112(10):1709–15. https://doi.org/10.1111/add.13763 .

Carbonell, X., & Panova, T. (2015). A critical consideration of social networking sites’ addiction potential. Addiction Research & Theory, 25(1). https://doi.org/10.1080/16066359.2016.1197915

Huang C. Social media addiction and personality: a meta-analysis. Asian J Soc Psychol. 2022;25(4):747–61. https://doi.org/10.1111/ajsp.12531 .

Griffiths, M. D., Kuss, D. J., & Demetrovics, Z. (2014). Chapter 6 – Social networking: an overview of preliminary findings. Behavioral Addictions, 119–141. https://doi.org/10.1016//B978-0-12-407724-9.00006-9

Fried, E. I. (2015). Problematic assumptions have slowed down depression research: why symptoms, not syndromes are the way forward. Frontiers in Psychology, 6. https://doi.org/10.3389/fpsyg.2015.00309

Borsboom D, Cramer AOJ. Network analysis: an integrative approach to the structure of psychopathology. Annu Rev Clin Psychol. 2013;9:91–121. https://doi.org/10.1146/annurev-clinpsy-050212-185608 .

https://doi.org/10.1002/eat.22890

Gomez R, Stavropoulos V, Zarate D, Griffiths MD. ADHD symptoms, the current symptom scale, and exploratory structural equation modelling: a psychometric study. Research in Developmental Disabilities. 2021;111:103850. https://doi.org/10.1016/j.ridd.2020.103850 .

Mullarkey MC, Marchetti I, Beevers CG. Using network analysis to identify central symptoms of adolescent depression. J Clin Child Adolesc Psychol. 2018;48(4):656–68. https://doi.org/10.1080/15374416.2018.1437735 .

Jones PJ, Ma R, McNally RJ. Bridge centrality: a network approach to understanding comorbidity. Multivar Behav Res. 2021;56(2):353–67. https://doi.org/10.1080/00273171.2019.1614898 .

Elliott H, Jones PJ, Schmidt U. Central symptoms predict posttreatment outcomes and clinical impairment in anorexia nervosa: A network analysis. Clinical Psychological Science. 2020;8(1):139–54. https://doi.org/10.1177/2167702619865958 .

Beard C, Millner AJ, Forgeard MJ, Fried EI, Hsu KJ, Treadway MT, Leonard CV, Kertz SJ, Björgvinsson T. Network analysis of depression and anxiety symptom relationships in a psychiatric sample. Psychology Medicine. 2016;46(16):3359–69. https://doi.org/10.1017/S0033291716002300 .

Article   CAS   Google Scholar  

Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. Qgraph: Network visualizations of relationships in psychometric data. Journal of Statistical Software, 48, 1–8. https://doi.org/10.18637/jss.v048.i04

Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods. 2018;50(1):195–212. https://doi.org/10.3758/s13428-017-0862-1 .

Hevey D. Network analysis: a brief overview and tutorial. Health Psychol Behav Med. 2018;6(1):301–28. https://doi.org/10.1080/21642850.2018.1521283 .

Hulme A, Stanton NA, Walker GH, Waterson P, Salmon PM. Are accident analysis methods fit for purpose? Testing the criterion-referenced concurrent validity of AcciMap, STAMP-CAST and AcciNet. Safety Science. 2021;144:105454. https://doi.org/10.1016/j.ssci.2021.105454 .

Stănculescu, E. The Bergen social media addiction scale validity in a Romanian sample using item response theory and network analysis. International Journal of Mental Health and Addiction, 1–8. https://doi.org/10.1007/s11469-021-00732-7

Zarate D, Hobson BA, March E, Griffiths MD, Stavropoulos V. Psychometric properties of the Bergen social media addiction scale: an analysis using item response theory. Addict Behav Rep. 2023;17:100473. https://doi.org/10.1016/j.abrep.2022.100473 .

Kovacs J, Zarate D, de Sena Collier G, Tran TTTD, Stavropoulos V. Disordered gaming: the role of a gamer’s distress profile. Can J Behav Sci. 2022. https://doi.org/10.1037/cbs0000335 .

Zarate D, Ball M, Montag C, Prokofieva M, Stavropoulos V. Unravelling the web of addictions: a network analysis approach. Addict Behav Rep. 2022;15:100406. https://doi.org/10.1016/j.abrep.2022.100406 .

Zarate, D., Dorman, G., Prokofieva, M., Morda, R., & Stavropoulos, V. (2023). Online behavioral addictions: longitudinal network analysis and invariance across men and women. Mind, Technology and Behavior . Advanced print.

Drapeau, A., Marchand, A., & Beaulieu-Prévost, D. (2012). Epidemiology of psychological distress, In Ed. L. LAbate, Mental Illness: Understanding, Prediction and Control. Books on Demand.

Marino C, Gini G, Vieno A, Spada MM. A comprehensive meta-analysis on problematic Facebook use. Comput Hum Behav. 2018;83:262–77. https://doi.org/10.1016/j.chb.2018.02.009 .

Keles E. Use of Facebook for the community services practices course: community of inquiry as a theoretical framework. Comput Educ. 2018;116:203–24. https://doi.org/10.1016/j.compedu.2017.09.003 .

Shensa A, Escobar-Viera CG, Sidani JE, Bowman ND, Marshal MP, Primack BA. Problematic social media use and depressive symptoms among U.S. young adults: a nationally-representative sample. Soc Sci Med. 2017;182:150–7. https://doi.org/10.1016/j.socscimed.2017.03.061 .

Yin L, Wang P, Nie J, Guo J, Feng J, Lei L. Social networking sites addiction and FoMO: the mediating role of envy and the moderating role of need to belong. Curr Psychol. 2021;40:3879–87. https://doi.org/10.1007/s12144-019-00344-4 .

Stadtfeld C, Snijders TA, Steglich C, van Duijn M. Statistical power in longitudinal network studies. Sociological Methods & Research. 2020;49(4):1103–32. https://doi.org/10.1177/0049124118769113 .

Constantin, M., Cramer, A. O. (2020). Sample Size Recommendations for Estimating Cross-Sectional Network Models. Available from https://arno.uvt.nl/show.cgi?fid=149724

Lovibond PF, Lovibond SH. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behavioral Research Therapy. 1995;33(3):335–43. https://doi.org/10.1016/0005-7967(94)00075-U .

Jones, P., & Jones, M. P. (2018). Package ‘networktools’ . Available from: https://cran.rproject.org/web/packages/networktools/networktools

Borgatti SP. Centrality and network flow. Social Networks. 2005;27(1):55–71. https://doi.org/10.1016/j.socnet.2004.11.008 .

Krämer N, Schäfer J, Boulesteix AL. Regularized estimation of large-scale gene association networks using graphical Gaussian models. BMC Bioinformatics. 2009;10(1):1–24. https://doi.org/10.1186/1471-2105-10-384 .

Robinaugh DJ, Millner AJ, McNally RJ. Identifying highly influential nodes in the complicated grief network. Journal of Abnormal Psychology. 2016;125(6):747. https://doi.org/10.1037/abn0000181 .

van Borkulo CD, van Bork R, Boschloo L, Kossakowski JJ, Tio P, Schoevers RA, Borsboom D, Waldorp LJ. Comparing network structures on three aspects: a permutation test. Psychological Methods, Advance Online Publication. 2022. https://doi.org/10.1037/met0000476 .

Stavropoulos, V., Vassallo, J., Burleigh, T. L., Gomez, R., & Colder Carras, M. (2021). The role of internet gaming in the association between anxiety and depression: a preliminary cross-sectional study. Asia-Pacific Psychiatry. https://doi.org/10.1111/appy.12474

Adams BLM, Stavropoulos V, Burleigh TL, Liew LWL, Beard CL, Griffiths MD. Internet gaming disorder behaviors in emergent adulthood: a pilot study examining the interplay between anxiety and family cohesion. Int J Ment Heal Addict. 2019;17:828–44. https://doi.org/10.1007/s11469-018-9873-0 .

Tullett-Prado D, Stavropoulos V, Gomez R, Doley J. Social media use and abuse: different profiles of users and their associations with addictive behaviors. Addict Behav Rep. 2023;17(4):100479. https://doi.org/10.1016/j.abrep.2023.100479 .

Sinha R. The role of stress in addiction relapse. Curr Psychiatry Rep. 2007;9:388–95. https://doi.org/10.1007/s11920-007-0050-6 .

Chen I-H, Pakpour AH, Leung H, Potenza MN, Su J-A, Lin C-Y, Griffiths MD. Comparing generalized and specific problematic smartphone/internet use: longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. J Behav Addict. 2020;9(2):410–9. https://doi.org/10.1556/2006.2020.00023 .

Chang C-W, Huang R-Y, Strong C, Lin Y-C, Tsai M-C, Chen I-H, Lin C-Y, Pakpour AH, Griffiths MD. Reciprocal relationships between problematic social media use, problematic gaming, and psychological distress among university students: A nine-month longitudinal study. Front Public Health. 2022;10:858482. https://doi.org/10.3389/fpubh.2022.858482 .

Chang K-C, Chang Y-H, Yen C-F, Chen J-S, Chen P-J, Lin C-Y, Griffiths MD, Potenza MN, Pakpour AH. A longitudinal study of the effects of problematic smartphone use on social functioning among people with schizophrenia: Mediating roles for sleep quality and self-stigma. J Behav Addict. 2022;11(2):567–76. https://doi.org/10.1556/2006.2022.00012 .

Joseph AM, Willenbring ML, Nugent SM, Nelson DB. A randomized trial of concurrent versus delayed smoking intervention for patients in alcohol dependence treatment. J Stud Alcohol. 2004;65(6):681–91. https://doi.org/10.15288/jsa.2004.65.681 .

Kwok C, Leung PY, Poon KY, Fung XCC. The effects of internet gaming and social media use on physical activity, sleep, quality of life and academic performance among university students in Hong Kong: a preliminary study. Asian J Soc Health Behav. 2021;4(1):36–44. https://doi.org/10.4103/shb.shb_81_20 .

Zarate D, Stavropoulos V, Ball M, de Sena Collier G, Jacobson NC. Exploring the digital footprint of depression: a PRSIMA systematic literature review of the empirical evidence. BMC Psychiatry. 2022;22:421. https://doi.org/10.1186/s12888-022-04013-y .

Patel VK, Chaudhary P, Kumar P, Vasavada DA, Tiwari DS. A study of correlates of social networking site addiction among the undergraduate health professionals. Asian J Soc Health Behav. 2021;4(1):30–5. https://doi.org/10.4103/shb.shb_1_21 .

Download references

Acknowledgements

VS has received the Australian Research Council, Discovery Early Career Researcher Grant/Award Number: DE210101107.

Author information

Authors and affiliations.

Institute for Health and Sport, Victoria University, Melbourne, Australia

Deon Tullett-Prado & Jo R. Doley

RMIT, Melbourne, Australia

Daniel Zarate & Vasileios Stavropoulos

Federation University, Ballarat, Australia

Rapson Gomez

National and Kapodistrian University of Athens, Athens, Greece

Vasileios Stavropoulos

You can also search for this author in PubMed   Google Scholar

Contributions

DT-P contributed to the article’s conceptualization, data curation, formal analysis, methodology, project administration, and writing of the original draft. JD, RG and VS contributed to the article’s conceptualization, data curation, writing, review, and editing the final draft and project administration. DZ contributed to the review and edit of the final form of the manuscript.

Corresponding author

Correspondence to Daniel Zarate .

Ethics declarations

Ethics approval and consent to participate.

Ethics approval granted by the Victoria University Ethics Committee. The current study only involved adult subjects (+ 18 years old) and informed consent was obtained in all cases. All methods were carried out in accordance with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

Dr Vasileios Stavropoulos is an associate editor for BMC Psychiatry. The remaining authors (Deon Tullett-Prado, Jo Doley, Rapson Gomez, and Daniel Zarate) declare no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion in this paper.

Additional information

Publisher’s note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ . The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/ ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and permissions

About this article

Cite this article.

Tullett-Prado, D., Doley, J.R., Zarate, D. et al. Conceptualising social media addiction: a longitudinal network analysis of social media addiction symptoms and their relationships with psychological distress in a community sample of adults. BMC Psychiatry 23 , 509 (2023). https://doi.org/10.1186/s12888-023-04985-5

Download citation

Received : 26 January 2023

Accepted : 26 June 2023

Published : 13 July 2023

DOI : https://doi.org/10.1186/s12888-023-04985-5

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Longitudinal network analysis
• Psychological distress
• Social media addiction

BMC Psychiatry

ISSN: 1471-244X

• Submission enquiries: [email protected]
• General enquiries: [email protected]

Search Query Search

Social media addiction: Its impact, mediation, and intervention

Vol.13, no.1 (2019).

Yubo Hou Dan Xiong Tonglin Jiang Lily Song Qi Wang

https://doi.org/10.5817/CP2019-1-4

Download Citation

This research examined the relations of social media addiction to college students' mental health and academic performance, investigated the role of self-esteem as a mediator for the relations, and further tested the effectiveness of an intervention in reducing social media addiction and its potential adverse outcomes. In Study 1, we used a survey method with a sample of college students (N = 232) and found that social media addiction was negatively associated with the students' mental health and academic performance and that the relation between social media addiction and mental health was mediated by self-esteem. In Study 2, we developed and tested a two-stage self-help intervention program. We recruited a sample of college students (N = 38) who met criteria for social media addiction to receive the intervention. Results showed that the intervention was effective in reducing the students’ social media addiction and improving their mental health and academic efficiency. The current studies yielded original findings that contribute to the empirical database on social media addiction and that have important theoretical and practical implications.

Crossref Cited-by

Advertisement

Social Media Addiction and Its Impact on College Students' Academic Performance: The Mediating Role of Stress

• Regular Article
• Published: 01 November 2021
• Volume 32 , pages 81–90, ( 2023 )

Cite this article

• Lei Zhao   ORCID: orcid.org/0000-0002-7337-3065 1 , 2  

7424 Accesses

1 Altmetric

Explore all metrics

Social media use can bring negative effects to college students, such as social media addiction (SMA) and decline in academic performance. SMA may increase the perceived stress level of college students, and stress has a negative impact on academic performance, but this potential mediating role of stress has not been verified in existing studies. In this paper, a research model was developed to investigate the antecedent variables of SMA, and the relationship between SMA, stress and academic performance. With the data of 372 Chinese college students (mean age 21.3, 42.5% males), Partial Least Squares, Structural Equation Model was adopted to evaluate measurement model and structural model. The results show that use intensity is an important predictor of SMA, and both SMA and stress have a negative impact on college students’ academic performance. In addition, we further confirmed that stress plays a mediating role in the relationship between SMA and college students’ academic performance.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save.

• Get 10 units per month
• Download Article/Chapter or Ebook
• 1 Unit = 1 Article or 1 Chapter
• Cancel anytime

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Similar content being viewed by others

Social media addiction and academic engagement as serial mediators between social anxiety and academic performance among college students

A serial mediation model of social media addiction and college students’ academic engagement: the role of sleep quality and fatigue

Associations Between Academic Motivation, Academic Stress, and Mobile Phone Addiction: Mediating Roles of Wisdom

Al-Yafi, K., El-Masri, M., & Tsai, R. (2018). The effects of using social network sites on academic performance: The case of Qatar. Journal of Enterprise Information Management , 31 (3), 446–462. https://doi.org/10.1108/JEIM-08-2017-0118 .

Andreassen, C. S., & Pallesen, S. (2014). Social network site addiction—an overview. Current Pharmaceutical Design, 20 , 1–9.

Article   Google Scholar  

Andreassen, C. S., Pallesen, S., & Griffiths, M. D. (2017). The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addictive Behaviors, 64 , 287–293. https://doi.org/10.1016/j.addbeh.2016.03.006

Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a facebook addiction scale. Psychological Reports, 110 (2), 501–517.

Argyris, Y. E., & Xu, J. (2016). Enhancing self-efficacy for career development in Facebook. Computers in Human Behavior, 55 (2), 921–931. https://doi.org/10.1016/j.chb.2015.10.023

Arnett, J. J. (2000). Emerging adulthood: A theory of development from the late teens through the twenties. American Psychologist, 55 (5), 469–480. https://doi.org/10.1016/j.chb.2015.10.023

Bano, S., Cisheng, W., Khan, A., & Khan, N. A. (2019). WhatsApp use and student’s psychological well-being: Role of social capital and social integration. Children and Youth Services Review, 103 , 200–208. https://doi.org/10.1016/j.childyouth.2019.06.002

Barclay, D., Higgins, C., & Thompson, R. (1995). The partial least squares (PLS) approach to causal modeling: Personal computer adoption and use as an illustration. Technology Studies, 2 (2), 285–309.

Google Scholar  

Bijari, B., Javadinia, S. A., Erfanian, M., Abedini, M. R., & Abassi, A. (2013). The impact of virtual social networks on students’ academic achievement in Birjand University of Medical Sciences in East Iran. Procedia Social and Behavioural Sciences, 83 , 103–106. https://doi.org/10.1016/j.sbspro.2013.06.020

Błachnio, A., Przepiorka, A., & Pantic, I. (2016). Association between facebook addiction, self-esteem and life satisfaction: A cross-sectional study. Computers in Human Behavior, 55 , 701–705. https://doi.org/10.1016/j.chb.2015.10.026

Brailovskaia, J., Rohmann, E., Bierhoff, H. W., Schillack, H., & Margraf, J. (2019). The relationship between daily stress, social support and Facebook addiction disorder. Psychiatry Research, 276 , 167–174. https://doi.org/10.1016/j.psychres.2019.05.014

Brailovskaia, J., Schillack, H., & Margraf, J. (2018). Facebook addiction disorder in Germany. Cyberpsychology, Behavior, and Social Networking, 21 (7), 450–456. https://doi.org/10.1089/cyber.2018.0140

Buil, I., Martínez, E., & Matute, J. (2016). From internal brand management to organizational citizenship behaviors: Evidence from frontline employees in the hotel industry. Tourism Management, 57 (12), 256–271. https://doi.org/10.1016/j.tourman.2016.06.009

Busalim, A. H., Masrom, M., & Zakaria, W. N. B. W. (2019). The impact of Facebook addiction and self-esteem on students’ academic performance: A multi-group analysis. Computers & Education, 142 , 103651. https://doi.org/10.1016/j.compedu.2019.103651

Carolus, A., Binder, J. F., Muench, R., Schmidt, C., Schneider, F., & Buglass, S. L. (2019). Smartphones as digital companions: Characterizing the relationship between users and their phones. New Media & Society, 21 , 914–938. https://doi.org/10.1177/1461444818817074

China Internet Network Information Center (CNNIC), 2020. The 44th Statistical Report on Internet Development in China (in Chinese). Retrieved August 28, 2020, from http://www.cac.gov.cn/2020-08/30/c_1124938750.htm

Cohen, S., Kamarck, T., & Mermelstein, R. (1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24 (4), 385–396. https://doi.org/10.2307/2136404

Dendle, C., Baulch, J., Pellicano, R., Hay, M., Lichtwark, I., Ayoub, S., & Horne, K. (2018). Medical student psychological distress and academic performance. Medical Teacher . https://doi.org/10.4236/ce.2018.99099

Doleck, T., & Lajoie, S. (2018). Social networking and academic performance: A review. Education and Information Technologies, 23 (1), 435–465. https://doi.org/10.1007/s10639-017-9612-3

Griffths, M. D., Kuss, D. J., & Demetrovics, Z. (2014). Social networking addictions: An overview of preliminary findings. Behavioral Addictions Criteria, Evidence, and Treatment , 119–141. https://doi.org/10.1016/B978-0-12-407724-9.00006-9 .

Hair, J. F. J., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) . Sage Publication.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of pls-sem. European Business Review, 31 (1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203

Hayes, A. F. (2009). Beyond Baron and Kenny: Statistical mediation analysis in the new millennium. Communication Monographs, 76 (4), 408–420. https://doi.org/10.1080/03637750903310360

Hormes, J. M., Kearns, B., & Timko, C. A. (2014). Craving Facebook? Behavioral addiction to online social networking and its association with emotion regulation deficits. Addiction, 109 (12), 2079–2088. https://doi.org/10.1111/add.12713

Hsiao, K. L., Shu, Y., & Huang, T. C. (2017). Exploring the effect of compulsive social app usage on technostress and academic performance: Perspectives from personality traits. Telematics and Informatics, 34 , 679–690. https://doi.org/10.1016/j.tele.2016.11.001

Jie, T., Yizhen, Y., Yukai, D., Ying, M., Dongying, Z., & Jiaji, W. (2014). Addictive behaviors prevalence of internet addiction and its association with stressful life events and psychological symptoms among adolescent internet users. Addictive Behaviors, 39 (3), 744–747. https://doi.org/10.1016/j.adolescence.2016.07.012

Juana, I., Serafín, B., Jesús, L., et al. (2020). Effectiveness of music therapy and progressive muscle relaxation in reducing stress before exams and improving academic performance in nursing students: A randomized trial. Nurse Education Today, 84 , 104217. https://doi.org/10.1016/j.nedt.2019.104217

Kimberly, Y. (2009). Facebook addiction disorder . The Center for Online Addiction.

Lau, W. W. F. (2017). Effects of social media usage and social media multitasking on the academic performance of university students. Computers in Human Behavior, 68 (3), 286–291. https://doi.org/10.1016/j.chb.2016.11.043

Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping . Springer.

Lee, W. W. S. (2017). Relationships among grit, academic performance, perceived academic failure, and stress in associate degree students. Journal of Adolescence, 60 , 148–152. https://doi.org/10.1016/j.adolescence.2017.08.006

Leung, L. (2007). Stressful life events, motives for Internet use, and social support among digital kids. CyberPsychology & Behavior, 10 (2), 204–214. https://doi.org/10.1089/cpb.2006.9967

Liang, C., Gu, D., Tao, F., Jain, H. K., Zhao, Y., & Ding, B. (2017). Influence of mechanism of patient-accessible hospital information system implementation on doctor-patient relationships: A service fairness perspective. Information & Management, 54 (1), 57–72.

Lim, K., & Jeoung, Y. S. (2010). Understanding major factors in taking Internet based lectures for the national college entrance exam according to academic performance by case studies. The Journal of the Korea Contents Association, 10 (12), 477–491. https://doi.org/10.5392/JKCA.2010.10.12.477

Lim, Y., & Nam, S.-J. (2016). Time spent on the Internet by multicultural adolescents in Korea. Asia Pacific Journal of Education, 37 (1), 55–68. https://doi.org/10.1080/02188791.2016.1169994

Lin, C. L., Jin, Y. Q., Zhao, Q., Yu, S. W., & Su, Y. S. (2021). Factors influence students’ switching behavior to online learning under covid-19 pandemic: A push-pull-mooring model perspective. The Asia-Pacific Education Researcher, 30 (3), 229–245. https://doi.org/10.1007/s40299-021-00570-0

Lovibond, P. F., & Lovibond, S. H. (1995). The structure of negative emotional states: Comparison of the depression anxiety stress scales (DASS) with the Beck depression and anxiety inventories. Behaviour Research and Therapy, 33 (3), 335–343. https://doi.org/10.1016/j.jad.2015.12.004

Marino, C., Gini, G., Vieno, A., & Spada, M. M. (2018). A comprehensive meta-analysis on problematic Facebook use. Computers in Human Behavior, 83 , 262–277. https://doi.org/10.1016/j.chb.2018.02.009

Masood, A., Luqman, A., Feng, Y., & Ali, A. (2020). Adverse consequences of excessive social networking site use on academic performance: Explaining underlying mechanism from stress perspective. Computers in Human Behavior . https://doi.org/10.1016/j.chb.2020.106476

McDaniel, B. (2015). ‘Technoference’: Everyday intrusions and inter-ruptions of technology in couple and family relationships. In C. Bruess Içinde (Ed.), Family communication in the age of digital and social media. Peter Lang Publishing.

Nayak, J. K. (2018). Relationship among smartphone usage, addiction, academic performance and the moderating role of gender: A study of higher education students in India. Computers and Education, 123 (5), 164–173. https://doi.org/10.1016/j.compedu.2018.05.007

O'Brien, L. (2012). Six ways to use social media in education. Retrieved from https://cit.duke.edu/blog/2012/04/six-ways-to-use-social-media-in-education/

Orosz, G., Istvan, T., & Beata, B. (2016). Four facets of Facebook intensity—the development of the multidimensional facebook intensity scale. Personality and Individual Differences, 100 , 95–104. https://doi.org/10.1016/j.paid.2015.11.038

Oye, N. D., Adam, M. H., & Nor Zairah, A. R. (2012). Model of perceived influence of academic performance using social networking. International Journal of Computers and Technology, 2 (2), 24–29. https://doi.org/10.24297/ijct.v2i1.2612

Pang, H. (2018). How does time spent on WeChat bolster subjective well-being through social integration and social capital? Telematics and Informatics, 25 , 2147–2156. https://doi.org/10.1016/j.tele.2018.07.015

Paul, J. A., Baker, H. M., & Cochran, J. D. (2012). Effect of online social networking on student academic performance. Computers in Human Behavior, 28 (6), 2117–2127. https://doi.org/10.1016/j.chb.2012.06.016

Peng, Y., & Li, J. (2021). The effect of customer education on service innovation satisfaction: The mediating role of customer participation. Journal of Hospitality and Tourism Management, 47 (5), 326–334. https://doi.org/10.1016/j.jhtm.2020.12.014

Phu, B., & Gow, A. J. (2019). Facebook use and its association with subjective happiness and loneliness. Computers in Human Behavior, 92 , 151–159. https://doi.org/10.1016/j.chb.2018.11.020

Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology , 88 (5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879 .

Prato, C. A., & Yucha, C. B. (2013). Biofeedback-assisted relaxation training to decrease test anxiety in nursing students. Nursing Education Perspectives, 34 (2), 76–81. https://doi.org/10.1097/00024776-201303000-00003

Rouis, S. (2012). Impact of cognitive absorption on facebook on students’ achievement. Cyberpsychology, Behavior, and Social Networking, 15 (6), 296–303. https://doi.org/10.1089/cyber.2011.0390

Ryan, T., Chester, A., Reece, J., & Xenos, S. (2014). The uses and abuses of facebook: A review of facebook addiction. Journal of Behavioral Addictions, 3 (3), 133–148. https://doi.org/10.1556/JBA.3.2014.016

Sandra, K., Dawans, B. V., Heinrichs, M., & Fuchs, R. (2013). Does the level of physical exercise affect physiological and psychological responses to psychosocial stress in women. Psychology of Sport and Exercise, 14 , 266–274. https://doi.org/10.1016/j.psychsport.2012.11.003

Shaw, M., & Black, D. W. (2008). Internet addiction . Springer.

Shi, C., Yu, L., Wang, N., Cheng, B., & Cao, X. (2020). Effects of social media overload on academic performance: A stressor–strain–outcome perspective. Asian Journal of Communication, 30 (2), 1–19. https://doi.org/10.1080/01292986.2020.1748073

Statista (2020). Number of social media users worldwide from 2010 to 2021 (in billions). Retrieved from https://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/

Tang, J. H., Chen, M. C., Yang, C. Y., Chung, T. Y., & Lee, Y. A. (2016). Personality traits, interpersonal relationships, online social support, and Facebook addiction. Telematics and Informatics, 33 (1), 102–108. https://doi.org/10.1016/j.tele.2015.06.003

Wang, J. L., Wang, H. Z., Gaskin, J., & Wang, L. H. (2015). The role of stress and motivation in problematic smartphone use among college students. Computers in Human Behavior, 53 , 181–188. https://doi.org/10.1016/j.chb.2015.07.005

Wartberg, L., Kriston, L., & Thomasius, R. (2020). Internet gaming disorder and problematic social media use in a representative sample of German adolescents: Prevalence estimates, comorbid depressive symptoms and related psychosocial aspects. Computers in Human Behavior, 103 , 31–36. https://doi.org/10.1016/j.chb.2019.09.014

Xu, X. A., Xue, K. N., Wang, L. L., Gursoy, D., & Song, Z. B. (2021). Effects of customer-to-customer social interactions in virtual travel communities on brand attachment: the mediating role of social well-being. Tourism Management Perspectives, 38 , 100790. https://doi.org/10.1016/j.tmp.2021.100790

Zhao, L. (2021). The impact of social media use types and social media addiction on subjective well-being of college students: A comparative analysis of addicted and non-addicted students. Computers in Human Behavior Reports, 4 (2), 100122. https://doi.org/10.1016/j.chbr.2021.100122

Zhao, L., Liang, C., & Gu, D. (2021). Mobile social media use and trailing parents’ life satisfaction: Social capital and social integration perspective. International Journal of Aging and Human Development, 92 (3), 383–405. https://doi.org/10.1177/0091415020905549

Download references

Acknowledgements

This study is supported by the Planning Subject for the 14th Five-year Plan of National Education Sciences (Grant No. EIA210425).

Author information

Authors and affiliations.

School of Management, Hefei University of Technology, Hefei, 230009, Anhui, China

Wendian College, Anhui University, Hefei, 230601, Anhui, China

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Lei Zhao .

Ethics declarations

Conflict of interest.

The authors declared no potential conflict of interest with respect to the research, authorship, and/or publication of this article.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Zhao, L. Social Media Addiction and Its Impact on College Students' Academic Performance: The Mediating Role of Stress. Asia-Pacific Edu Res 32 , 81–90 (2023). https://doi.org/10.1007/s40299-021-00635-0

Download citation

Accepted : 21 October 2021

Published : 01 November 2021

Issue Date : February 2023

DOI : https://doi.org/10.1007/s40299-021-00635-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Use intensity
• Social media addiction
• Academic performance
• Find a journal
• Publish with us
• Track your research

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here .

Loading metrics

Open Access

Peer-reviewed

Research Article

Antecedents of social media addiction in high and low relational mobility societies: Motivation to expand social network and fear of reputational damage

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Visualization, Writing – original draft

* E-mail: [email protected]

Affiliation Graduate School of Humanities and Sociology, The University of Tokyo, Tokyo, Japan

Roles Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Writing – review & editing

Affiliation Graduate School of Human Sciences, Osaka University, Osaka, Japan

• Shuma Iwatani, 
• Eiichiro Watamura

• Published: April 18, 2024
• https://doi.org/10.1371/journal.pone.0300681
• Reader Comments

Contrary to previous studies on the antecedent factors of social media addiction, we focused on the social environmental factor of relational mobility (i.e., the ease of constructing new interpersonal relationships) and investigated its relationship with social media addiction. People in low relational mobility societies have fewer opportunities to select new relationship partners and consequently feel a stronger need to maintain their reputation. We hypothesized that (1) people in low relational mobility societies are more strongly addicted to social media because they estimate that greater reputational damage will be caused by ignoring messages and (2) people in low relational mobility societies estimate greater reputational damage than actual damage. We conducted two online experiments with 715 and 1,826 participants. Our results demonstrated that (1) there is no relationship between relational mobility and social media addiction and (2) people in both high and low relational mobility societies overestimate reputational damage. Furthermore, we demonstrated that the social media addiction mechanism differs between societies: (3) people in low relational mobility societies estimate greater reputational damage, whereas (4) people in high relational mobility societies are more motivated to expand their social networks; both mechanisms strengthen their social media addiction. Based on these results, we propose interventions for moderating social media addiction in both high and low relational mobility societies.

Citation: Iwatani S, Watamura E (2024) Antecedents of social media addiction in high and low relational mobility societies: Motivation to expand social network and fear of reputational damage. PLoS ONE 19(4): e0300681. https://doi.org/10.1371/journal.pone.0300681

Editor: Giulia Ballarotto, University of Rome La Sapienza: Universita degli Studi di Roma La Sapienza, ITALY

Received: August 9, 2023; Accepted: March 1, 2024; Published: April 18, 2024

Copyright: © 2024 Iwatani, Watamura. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: Data and R code are publicly available via the Open Science Framework and can be accessed at https://osf.io/ch5py/

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1. Introduction

Social media platforms have both positive and negative effects on people [ 1 ]. On the positive side, it allows people to obtain information, send messages from anywhere, and communicate more closely with others. On the negative side, some users are addicted to social media and spend excessive time on it. In this study, we attempted to demonstrate the mechanism through which people become addicted to social media.

The term "addiction" has various meanings, and its definition has expanded over the years. It can be classified into two main categories: substance addiction and non-substance addiction [ 2 ] (non-substance addiction is also referred to as behavioral addiction [ 3 ]). Substance addiction is a neuropsychiatric disorder characterized by a recurring desire to ingest substances, such as drugs or alcohol, despite harmful consequences [ 2 , 4 ]. People who require daily intake of alcohol are defined as being addicted to alcohol. By contrast, non-substance addiction refers to addiction to things other than substances [ 5 – 7 ], such as pathological gambling, the Internet, and mobile phones [ 2 ].

Social media addiction is a type of non-substance addiction. The following are some definitions of social media addiction: “irrational and excessive use of social media to the extent that it interferes with other aspects of daily life” [ 5 ], “excessive use and habitual monitoring of social media, manifested in compulsive usage that comes at the expense of other activities” [8, p.747], and “being overly concerned about SNSs, to be driven by a strong motivation to log on to or use SNSs, and to devote so much time and effort to SNSs that it impairs other social activities, studies/job, interpersonal relationships, and/or psychological health and well-being” ([ 9 ], p.4054; SNS: social network service). The common items in these definitions are (i) devoting excessive time to social media and (ii) the negative consequences of using social media (i.e., interfering with other social activities such as studying, job, interpersonal relationships, psychological health, and well-being).

Social media addiction has various negative effects, including damaging mental health [ 5 ], poor life satisfaction [ 10 ], and chronic physical issues, such as neck pain or headaches [ 11 ]. Some studies that focused on company employees have demonstrated that social media addiction leads to a reduction in sleeping hours [ 12 ], increased distraction at workplace [ 12 ], and impaired productivity [ 8 ].

In this study, we used the term “addiction” or “social media addiction” in a non-clinical sense. This is because social media addiction is not included in the DSM-5-TR classification [ 13 ] and no study has demonstrated that social media addiction can have severe physical consequences [ 14 ].

1.1. Antecedents of social media addiction

Previous studies have investigated various antecedents of social media addiction such as neuroticism [ 15 ], lack of self-control [ 16 ], and extraversion [ 17 ], and have several perspectives on social media addiction [ 18 ]. One perspective focuses on dispositional differences such as attachment styles. D’Arienzo et al. [ 19 ] concluded that avoidant or insecure attachment style is associated with stronger social media addiction. Additionally, an empirical study by Ballarotto et al. [ 20 ] demonstrated that individuals who are less attached to their parents are more strongly addicted to Instagram. Eroglu [ 21 ] showed that people with insecure attachments (i.e., those having negative “internally working models about both themselves and others” [ 21 ] p.151) are more strongly addicted to Facebook. Additionally, Monacis et al. [ 22 ] demonstrated that people with avoidant attachment style (i.e., those who experience discomfort with intimacy) are more strongly addicted to social media.

Furthermore, some studies have focused on the motivation to use social media. For example, those who feel lonely are more strongly addicted to social media [ 23 ] as they are motivated to connect with others [ 24 ]. Moreover, extraverts are more strongly addicted to social media [ 17 ] as they use it to expand their social connections [ 25 ]. Additionally, those with a higher motivation to expand their social network would be more strongly addicted to social media, as it allows them to maintain or expand their social network.

Additionally, demographic variables, such as sex and age, may be related to social media addiction Mari et al. [ 26 ] found that females are more strongly addicted to the Internet, whereas Su et al. [ 27 ] and Alnjadat et al. [ 28 ] found that males are more strongly addicted to the Internet or social media. Moreover, age is related to social media addiction as younger individuals are more strongly addicted to social media [ 25 ].

Also, distressing changes in social situations, such as those during and after the COVID-19 pandemic, may also strengthen social media addiction. Recent studies have noted that the importance of social media as a medium for rapid information dissemination has increased after COVID-19 [ 29 ] and demonstrated that psychological distress owing to COVID-19 has strengthened social media [ 30 ], Internet [ 20 ], and Instagram [ 20 ] addictions, and social media addiction has also increased the likelihood of experiencing depression [ 31 ]. These studies imply that distressing situations and social media addiction have mutually strengthened each other, especially after the COVID-19 pandemic.

Although these studies focused on micro-level factors, such as depression or distress, the effect of macro-level social environmental factors on social media addiction is understudied and must be further investigated [ 18 ]. Based on Sun et al. [ 18 ]’s suggestion, we focused on a social environmental factor (i.e., relational mobility [ 32 ]) and investigated the relationship between the social environment and social media addiction.

We conducted two studies to examine the effect of the social environment (i.e., relational mobility) on social media addiction. Relational mobility of a society refers to how easily people in the society can select new relationship partners when necessary [ 32 ]. Relational mobility is lower in typical rural areas wherein interpersonal relationships are closed to outsiders. As relational mobility affects the sensitivity of an individual to social rejection [ 33 , 34 ], it can affect their interpersonal behavior. As an example of interpersonal behavior on social media, we focused on a message exchange situation and examined whether the estimation of reputational damage incurred by ignoring messages moderates the relationship between relational mobility and social media addiction.

Fig 1 illustrates our conceptual model. In Study 1, we examined the following mediation process: people in lower relational mobility societies estimate that greater reputational damage is incurred by message ignorance, which strengthens their social media addiction. This model was proposed based on previous studies that have indicated that people in lower relational mobility societies are more sensitive to social rejection [ 33 , 34 ], and they make decisions based on their estimations of others’ attitudes [ 35 ].

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0300681.g001

In Study 2, we additionally examined the following dual paths: (1) the mediating effect of estimated reputational damage on the negative relationship between relational mobility and social media addiction and (2) the mediating effect of extroversion, the motivation to expand the social network, and loneliness on the positive relationship between relational mobility and social media addiction. We focused on extroversion, motivation to expand social network, and loneliness because they are related to both relational mobility and social media addiction.

In the following section, we review studies on relational mobility, develop our hypotheses, and outline our contributions.

1.2. Relational mobility and social media addiction

We first focused on the social environmental factor of relational mobility [ 32 ], which is a sociological variable that refers to “the amount of opportunities people have in a given society or social context to select new relationship partners when necessary” [ 32 ]. Relational mobility is lower in typical rural areas, where people exclusively develop intimate relationships with neighbors and seldom construct new relationships with outsiders, whereas it is higher in typical urban areas, where people have weaker social ties and entering or leaving relationships is easier.

Thus, people in low relational mobility societies cannot easily construct alternate relationships, even when they earn a bad reputation and are excluded from their communities. Therefore, the consequences of earning a bad reputation are worse for people in low relational mobility societies [ 36 ], which strengthens their need to avoid reputational damage. Indeed, people in lower relational mobility societies are more sensitive to social rejection [ 33 , 34 ] and refrain from sharing personal information, such as embarrassing experiences or failures [ 37 ].

Based on these studies, we assume that people in lower relational mobility societies are more strongly addicted to social media. Additionally, as they are more sensitive to social rejection, they have more difficulty ignoring messages on social media and thus spend more time on social media, resulting in higher addiction.

• Hypothesis 1 : People in low relational mobility societies are more strongly addicted to social media.

1.3. Mediating effect of estimated reputational damage

People in lower relational mobility societies estimate greater reputational damage incurred by ignoring messages on social media because, as described in the Section 1.2., they are more sensitive to social rejection [ 33 , 34 ]. In some cases, this sensitivity might result in an overestimation of reputational damage, leading them to unnecessarily respond to messages on social media. However, in other cases, this sensitivity could reduce the possibility that they underestimate the damage in situations where ignoring them could lower their reputation, and mistakenly ignore messages and damage their reputation. Therefore, estimating greater reputational damage and refraining from ignoring messages are adaptive in that this estimation ( overestimation in some cases) can lower the possibility of damaging their reputation. However, this estimation can strengthen their addiction to social media. Because forming and maintaining strong and stable interpersonal relationships is important to humans [ 38 ], people make decisions based on their estimations of others’ attitudes [ 35 ]. For example, people are more likely to follow norms when they estimate that deviating from them will tarnish their reputation [ 39 ]. Based on these studies, we assumed that people in lower relational mobility societies estimate greater reputational damage incurred by ignoring messages, which strengthens their social media addiction.

• Hypothesis 2 : The estimation of greater reputational damage incurred by ignoring messages mediates the negative relationship between relational mobility and social media addiction.

1.4. Accuracy of reputational damage estimation

Hypothesis 2 focuses on the mediating effect of estimated reputational damage. In this section, we examine two questions: do people in both high and low relational mobility societies accurately estimate reputational damage?

Based on [ 36 ], we hypothesized that people in low relational mobility societies overestimate the possibility of earning a bad reputation. Overestimation of reputational damage can help them avoid situations wherein they mistakenly estimate that performing detrimental actions would not damage their reputation when it would. An example of this situation on social media is that people mistakenly estimate that ignoring messages will not tarnish their reputation, even if it does. If they ignore messages based on this underestimation, they will gain a bad reputation, the cost of which is higher in lower relational mobility societies. Therefore, people in lower relational mobility societies are more likely to overestimate reputational damage, especially because interpersonal relationships in these societies are closed and the cost of earning a bad reputation is higher. Indeed, Iwatani and Muramoto [ 39 ] focused on community activities, such as cleanup drives, and demonstrated that people in low relational mobility societies overestimate the possibility of gaining a bad reputation, whereas those in high relational mobility societies estimate it accurately. In this study, we extend their findings to the context of social media and investigate the following hypothesis:

• Hypothesis 3 : People in low relational mobility societies overestimate the possibility of receiving a bad evaluation by ignoring messages, whereas people in high relational mobility societies do not.

1.5. Contributions of this study

We believe our study has at least three original contributions. First, it highlights the effects of the social environment on social media addiction. The human mind, including cognition, emotion, and motivation, is affected by both cultural [ 40 ] and social environments [ 41 ]; therefore, social environmental factors can impact social media addiction. However, few studies have investigated the effect of social environments on social media addiction [ 42 ] (however, see [ 43 ], wherein the effect of relational mobility on problematic Internet use was investigated). The novelty of this study lies in its investigation of social media addiction from the perspective of socio-ecological psychology.

Second, we focus on interpersonal interactions between social media users as an antecedent of social media addiction, whereas previous studies have primarily focused on individual psychological factors [ 15 – 17 ]. The originality of our study lies in the fact that we focus on miscommunications between social media users, that is, inaccurately (over)estimated reputational damage as an antecedent factor of social media addiction.

Third, our study also features originality for studies on socio-ecological psychology in that we investigate the effect of relational mobility on online behavior. Previous studies have demonstrated the effect of relationality on general trust, self-esteem, and intimacy with close friends [ 44 ]. However, few studies have demonstrated the effect of offline relational mobility on the online psychological tendencies of humans, except for Dong et al. [ 43 ] and Thomson et al. [ 45 ], who examined the effect of relational mobility on problematic Internet use or online privacy concerns of people.

We conducted two studies to examine the following hypotheses: (1) the direct effect of the social environment (relational mobility) on social media addiction, (2) the mediating effect of reputational damage estimation on the relationship between the social environment and social media addiction, and (3) the accuracy of reputational damage estimation.

2. Materials and methods

2.1. study 1.

We tested the proposed hypotheses using the LINE message exchange service, which is the most popular social media platform in Japan, wherein it is used by approximately 80% of the Internet users [ 46 ]. LINE was considered suitable for this study because it provides a "read notification function," through which users can determine whether their messages have been read and ignored. Additionally, they are aware that their messaging partner can determine if their messages are ignored.

The experiment included two conditions: (1) wherein participants ignored messages (ignorer condition) and (2) wherein participants’ messages were ignored (ignored condition). In the ignorer condition, participants imagined a scenario wherein they read the messages received on LINE and ignored them. They estimated how message senders would evaluate the participants when participants themselves ignored messages (estimated reputational damage). In the ignored condition, participants imagined a scenario wherein they sent messages and the receiver read and ignored them, and evaluated the receiver who ignored the messages (actual reputational damage). We compared the estimated and actual reputational damage and investigated whether people from lower relational mobility societies estimated more reputational damage than the actual damage. We also investigated whether people from lower relational mobility societies estimated higher reputational damage and were more strongly addicted to social media.

2.1.1. Participants.

This study was approved by the Ethics Review Committee of the University of Tokyo. Written informed consent was obtained from all participants. They were recruited through a crowdsourcing service (Yahoo! Crowdsourcing; https://crowdsourcing.yahoo.co.jp/ ) between February 19 and 20, 2022. They were informed of the purpose of this study, and only those who agreed to participate (i.e., those who clicked “agree”) proceeded to answer the questions. Study 1 included 715 participants.

Study 1 was conducted using the between-participant design. We excluded 54 participants who did not pass the instructional manipulation check [ 47 ], 126 who did not use LINE, and 27 who had no friends whom they could contact privately through LINE. We also excluded data with missing values and one participant who answered that their age was 3. Finally, we analyzed the data of 453 participants (males: 308, females: 138, and others: 7). Their average age was 46.46 years ( SD = 11.17).

Half of the participants were randomly assigned to the ignorer condition, and the other half were randomly assigned to the ignored condition, resulting in 222 and 231 participants assigned to the ignorer and ignored conditions, respectively.

We examined whether the sample size was sufficiently large using G*Power version 3.1.9.7 [ 48 ] to conduct a post-hoc power analysis, assuming f = 0.05 (small to medium effect size), α = 0.05, N = 453, and three predictors (condition, relational mobility, and the interaction between them). The calculated power of the test was 0.99, which indicated that the sample size was adequate.

2.1.2. Reputational damage estimation (ignorer condition).

The participants were first asked to write the first-name initials of one of their friends they had privately contacted. The friend’s name is denoted as Mr. A in this study (It was denoted as “A-san” in our actual question). Participants assigned to the ignorer condition were asked to read and imagine a scenario wherein they received a message from Mr. A that stated that they had to discuss something, read it, but did not reply for two or three days.

After participants read the scenario, they estimated Mr. A’s evaluation of them by answering the following six items, extracted from a previous study [ 49 ], on a six-point Likert scale, ranging from 1 (“strongly disagree”) to 6 (“strongly agree”): “Mr. A would think you are a bad person,” “Mr. A would think you are an untrustworthy person,” “Mr. A would think you are an honest person,” “Mr. A would think they do not want to be your friend anymore,” “Mr. A would think they cannot feel secure with you,” and “Mr. A would think you are a cunning person.” We calculated the reputational damage estimation score by averaging the sum of the scores (α = 0.89, M = 2.89, SD = 0.99).

2.1.3. Participants’ evaluation (ignored condition).

Participants assigned to the ignored condition were asked to read and imagine a scenario wherein they sent a message to Mr. A stating that they had to discuss something, Mr. A received and read it but did not reply for two or three days.

After reading the scenario, participants answered questions regarding their evaluation of Mr. A. The items were almost the same as those in the previous scenario, and only the subjects were changed. For example, we changed the item “Mr. A would think you are a bad person” to “I think Mr. A is a bad person.” We again calculated the evaluation score by averaging the sum of the scores (α = 0.89, M = 2.26, SD = 0.88).

2.1.4. Social media addiction.

We used the social media addiction questionnaire (SMAQ; 7-point scale), which is composed of eight items and was proposed by Hawi and Samaha [ 10 ]. We changed the term “social media” in SMAQ to “LINE” for this study. For example, the question “I often think about social media when I am not using it” was modified to “I often think about LINE when I am not using it.” As in [ 10 ], we calculated the social media addiction score by averaging the sum of the scores (α = 0.86, M = 2.73, SD = 1.02). As there was no threshold to distinguish between those addicted to social media and those who were not [ 10 ], we did not perform threshold-based distinguishing between those who were addicted to LINE and those who were not. Participants were considered to be more strongly addicted to LINE if they scored higher on this scale.

2.1.5. Relational mobility.

Relational mobility was measured using the relational mobility scale [ 32 ]. Participants were presented with 12 statements and asked how much they agreed with them based on a six-point Likert scale, from 1 (“strongly disagree”) to 6 (“strongly agree”). The statements included the following: “they (people in the immediate society (your school, workplace, town, neighborhood, etc.) in which you live) have many chances to get to know other people.” The relational mobility score was calculated by averaging the sums of the scores (α = 0.75, M = 3.60, SD = 0.51). The relational mobility of the participant’s society was considered to be higher if they scored higher on this scale.

2.2. Study 2

Although Study 1 only investigated the factors that mediate the negative relationship between relational mobility and social media addiction, Study 2 investigated the factors that mediate the positive relationship between the two. We focused on the following three factors: loneliness, extroversion, and the motivation to expand social network. We examined whether these three factors mediated the positive relationship between relational mobility and social media addiction, which would cancel out the negative relationship examined in Study 1.

First, we focused on loneliness. We assumed that the relationship between relational mobility and loneliness was positive based on the study by Oishi et al. [ 50 ], which demonstrated that people in mobile conditions (wherein they imagined that they would move to a different location every other year) experienced more loneliness than those in stable conditions (wherein they imagined that they would stay in the same city for at least ten years). Additionally, there is a positive relationship between loneliness and social media addiction [ 23 ], which suggests that loneliness mediates a positive relationship between relational mobility and social media addiction.

Next, we focused on extroversion. There is a positive relationship between the within-state migration level and extroversion [ 51 ], which implies that there is a positive relationship between relational mobility and extroversion. Additionally, there is a positive relationship between extroversion and social media addiction [ 17 ]. These findings suggest that extroversion mediates the positive relationship between relational mobility and social media addiction.

Finally, we focus on the motivation to expand social network. An experimental study demonstrated that people in the mobile condition are more motivated to expand their social networks than those in the stable condition [ 50 ]. In addition, extraverts have larger social networks, which can promote their use of social media [ 4 ]. These findings suggest that the motivation to expand the social network mediates the positive relationship between relational mobility and social media addiction. In summary, we developed the following additional hypotheses and examined the model presented in Fig 1 .

• Hypothesis 4a : Loneliness mediates the positive relationship between relational mobility and social media addiction.
• Hypothesis 4b : Extroversion mediates the positive relationship between relational mobility and social media addiction.
• Hypothesis 4c : Motivation to expand social network mediates the positive relationship between relational mobility and social media addiction.

2.2.1. Participants.

This study was approved by the Ethics Review Committee of the University of Tokyo. Written informed consent was obtained from all the participants. Study 2 employed the within-participants design and included 1826 participants, recruited through the same crowdsourcing service (Yahoo! Crowdsourcing; https://crowdsourcing.yahoo.co.jp/ ) between August 26 and 27, 2022. They were informed of the purpose of this study, and only those who agreed to participate proceeded to answer the questions. We excluded 143 participants who did not pass the instructional manipulation check [ 47 ], 309 who did not use LINE, and 209 who had no friends whom they could contact privately through LINE. We also excluded data with missing values and eventually analyzed the data of 1065 participants (males: 670, females: 374, and others: 21). Their average age was 48.11 years ( SD = 12.09).

We examined whether the sample size was sufficiently large by conducting a post-hoc power analysis, assuming a root mean square error of approximation (RMSEA) in the null hypothesis = 0.05, RMSEA in the alternative hypothesis = 0.01, degrees of freedom = 7, N = 1065, and α = 0.05. The calculated power was 0.85, which indicated that the sample size was adequate.

2.2.2. Measurements.

As in Study 1, participants were asked to write the first-name initials of their friends they had privately contacted, who were denoted as Mr. A. Thereafter, they were asked to imagine the following scenarios: (1) wherein they ignored messages and (2) wherein their messages were ignored.

2.2.3. Reputation damage estimation.

The participants read the same scenario as in Study 1 (ignorer condition), wherein they ignored Mr. A’s message, and answered the following five items on a six-point Likert scale based on a previous study [ 49 ], ranging from 1 (“strongly disagree”) to 6 (“strongly agree”): “Mr. A would think you are a bad person,” “Mr. A would think you are an untrustworthy person,” “Mr. A would think they cannot feel secure with you,” “Mr. A would think you are an unreliable person,” and “Mr. A would not want to deepen their friendship with you.” We calculated the reputational damage estimation score by averaging the sums of the scores (α = 0.96, M = 3.16, SD = 1.19).

2.2.4. Participants’ evaluation.

Next, the participants read the same scenario as in Study 1 (ignored condition), wherein Mr. A ignored their messages. Thereafter, they responded with their evaluations of Mr. A. These items were almost the same as those mentioned above, and only their subjects were changed. For example, we changed the item “Mr. A would think you are a bad person” to “I think Mr. A is a bad person.” We calculated the evaluation score by averaging the sum of the scores (α = 0.96, M = 2.68, SD = 1.14).

2.2.5. Social media addiction.

We used the same questionnaires as in Study 1 to calculate the social media addiction scores. The sum of the scores were averaged (α = 0.86, M = 2.69, SD = 1.03).

2.2.6. Extroversion.

We measured extroversion using the Ten-Item Personality Inventory assessment [ 52 ]. The participants were asked to answer the following two items on a seven-point Likert scale, ranging from 1 (“strongly disagree”) to 7 (“strongly agree”): “I see myself as extraverted, enthusiastic” and “I see myself as reserved, quiet” (reverse-scored item). We calculated the extroversion score by averaging the sums of the scores ( r = 0.47, p < 0.01, M = 3.55, SD = 1.31). A participant was considered more extraverted if they scored higher.

2.2.7. Motivation to expand social network.

We measured the motivation to expand the social network using a seven-point Likert scale [ 50 ]. The questionnaire was composed of four items (e.g., “eager to make friends,” “want to meet new people”). We calculated the motivation to expand the social network by averaging the sum of the scores (α = 0.92, M = 3.52, SD = 1.37). Participants were considered to have higher motivation to expand their social networks if they scored higher on this scale.

2.2.8. Loneliness.

We measured loneliness using a five-point Likert scale [ 53 ]. The scale was composed of six statements (e.g., “I usually sense an experience of emptiness,” “I often feel missing close people around me”). We calculated the loneliness score by averaging the sum of the scores (α = 0.85, M = 2.82, SD = 0.76). Participants were considered to be lonelier if they scored higher on this scale.

2.2.9. Relational mobility.

As stated in Section 2.1.5, relational mobility score was measured using the relational mobility scale [ 32 ]. It was calculated by averaging the sum of the scores (α = 0.78, M = 3.66, SD = 0.53).

2.3. Statistical analysis

We used R version 4.3.2 for statistical analyses. For mediation analyses, multiple regression analysis, and generalized linear mixed model analysis, the statistical significance standard was set as p = .05, whereas for structural equation modeling (SEM), the statistical significance standard for the model fit was set as RMSEA = .05.

Study 1 was conducted using a between-participants design ( ignorer and ignored conditions). To test Hypotheses 1 and 2, we analyzed participants in the ignorer condition, (i.e., those who answered reputational damage estimation) and conducted a mediation analysis using the bootstrap method (5000 samples) to examine whether the negative effect of relational mobility on social media addiction was mediated by reputational damage estimation. This analysis was conducted after centering all variables in the model.

For testing Hypothesis 3, we conducted a multiple regression analysis. The evaluation was predicted using a dummy evaluator variable ( ignored condition (i.e., participants’ actual reputational damage) = 0, ignorer condition (i.e., estimated reputational damage from others) = 1), relational mobility, and the interaction between them. This analysis was also conducted after centering all variables in this model.

Study 2 employed a within-participant design. Participants read both the ignorer and ignored condition scenarios. For testing Hypothesis 2 and Hypotheses 4a–c, we employed SEM techniques and examined the following hypotheses: estimation of greater reputational damage mediates the negative relationship between relational mobility and social media addiction, whereas loneliness, extroversion, and motivation to expand social networks mediates the positive relationship between them ( Fig 1 ).

For testing Hypothesis 3, we used a generalized linear mixed model with random intercepts for the participants to examine our hypothetical model. The evaluation toward the ignorer was predicted using the dummy evaluator variable ( ignored condition = 0, ignorer condition = 1), relational mobility, and the interaction between them. This analysis was conducted after centering all variables in this model.

3. Results and discussion

3.1. study 1, 3.1.1. are people in lower relational mobility societies more addicted to social media.

First, we examined Hypotheses 1 and 2: (1) people in low relational mobility societies are more strongly addicted to social media (Hypothesis 1) and (2) the estimation of greater reputational damage incurred by ignoring messages would mediate the negative relationship between relational mobility and social media addiction (Hypothesis 2).

We only analyzed the answers of participants in the ignorer condition because we did not measure the estimated reputational damage in the ignored condition. After centering all variables in the model, we conducted a mediation analysis using the bootstrap method (5000 samples) to examine whether the effect of relational mobility on social media addiction was mediated by reputational damage estimation. Relational mobility had a significant effect on reputational damage estimation, indicating that people in low relational mobility societies estimated a higher reputational damage caused by ignoring messages (β = -0.15, p = 0.04). The effect of reputational damage estimation on social media addiction was not statistically significant (β = 0.14, p = 0.06). Additionally, the direct effect of relational mobility on social media addiction was not significant (β = -0.10, p = 0.18). These results did not support Hypotheses 1 and 2. However, consistent with Hypothesis 2, there was a statistically significant negative correlation between relational mobility and reputational damage estimation ( r = -0.15, p = 0.03) as well as a statistically significant positive correlation between reputational damage estimation and social media addiction ( r = 0.16, p = 0.02), although there was no statistically significant correlation between relational mobility and social media addiction ( r = -0.12, p = 0.07).

Thereafter, we conducted an additional analysis using age and sex (male = 0, female = 1) as control variables. To examine the effect of sex, seven participants who did not answer “male” or “female” were excluded. The effect of relational mobility on reputational damage estimation was statistically significant (β = -0.14, p = 0.05). In contrast to the aforementioned analysis, the effect of reputational damage estimation on social media addiction was also statistically significant (β = 0.15, p = 0.04). The direct effect of relational mobility on social media addiction was not significant (β = -0.09, p = 0.28) as in the above analysis. Additionally, the main effect of sex (β = 0.14, p = 0.03) was statistically significant, whereas that of age (β = -0.09, p = 0.23) was not.

3.1.2. Do people in low relational mobility societies overestimate reputational damage?

Next, we examined Hypothesis 3: people in low relational mobility societies overestimate the possibility of receiving a bad evaluation incurred by ignoring messages, whereas people in high relational mobility societies do not.

Prior to the analysis, we constructed a dummy variable for the evaluator (participants’ actual reputational damage = 0, estimated reputational damage from others = 1). After centering all variables in this model, we conducted a multiple regression analysis. The evaluation was predicted using the dummy evaluator variable, relational mobility, and the interaction between the two.

The main effect of evaluator was significant (β = 0.31, p < 0.01), but the interaction effect between the evaluator and relational mobility was not (β = -0.02, p = 0.60). These results imply that people in low relational mobility societies estimate greater reputational damage incurred by ignoring messages than the actual damage (consistent with Hypothesis 3), and the same holds true for people in high relational mobility societies (inconsistent with Hypothesis 3).

The main effect of relational mobility was also significant (β = -0.13, p < 0.01), which implies that (1) people who ignored messages were evaluated more negatively in low relational mobility societies than in high relational mobility societies and (2) people in low relational mobility societies estimated that ignoring messages would incur higher reputational damage than people in high relational mobility societies.

We conducted an additional analysis using age and sex (male = 0, female = 1) as control variables. To examine the effect of sex, seven participants who did not answer “male” or “female” were excluded. The results were the same as those obtained in the aforementioned analysis. The main effects of the evaluator (β = 0.31, p < 0.01) and relational mobility (β = -0.12, p = 0.01) were statistically significant, whereas the interaction effect between the evaluator and relational mobility was not (β = -0.03, p = 0.56). Additionally, the main effects of age (β = -0.08, p = 0.07) and sex (β = -0.02, p = 0.68) were not significant.

3.1.3. Moderating effect of age.

We exploratively examined the moderating effect of age; whether the effect of reputational damage estimation on social media addiction differed depending on age. A multiple regression analysis was conducted after entering all variables in the model. Social media addiction was used as the dependent variable, whereas relational mobility, reputational damage estimation, sex, age, and the interaction between reputational damage estimation and age were used as independent variables. The main effects of reputational damage estimation (β = 0.15, p = 0.02) and sex (β = 0.14, p = 0.04) were statistically significant, whereas those of relational mobility (β = -0.08, p = 0.22) and age (β = -0.09, p = 0.20) were not. Additionally, the interaction effect was not significant (β = 0.01, p = 0.84).

3.1.4. Discussion.

In Study 1, we examined (1) the effect of relational mobility on social media addiction (Hypothesis 1), (2) mediating effect of reputational damage estimation on the relationship between relational mobility and addiction (Hypothesis 2), and (3) accuracy of reputational damage estimation (Hypothesis 3).

First, we found that both relational mobility and reputational damage estimation had no effect on social media addiction; these results do not support Hypothesis 1. Second, we found that people in lower relational mobility societies estimated higher reputational damage. We also found a positive correlation between reputational damage estimation and social media addiction. These results were consistent with Hypothesis 2 (the mediating effect of reputational damage estimation), but this hypothesis was not supported because no direct relationship between relational mobility and social media addiction was observed. These results imply that other factors mediate the positive relationship between relational mobility and social media addiction, which negates the negative relationship between them. We further examined this possibility in Study 2. Third, we found that people overestimate the reputational damage caused by ignoring messages. This result partially supported Hypothesis 3, in that people in low relational mobility societies overestimate reputational damage incurred by ignoring messages, but contradicted Hypothesis 3, in that people in high relational mobility societies also overestimate it.

3.2. Study 2

3.2.1. relationship between relational mobility and social media addiction..

We investigated the relationship between relational mobility and social media addiction using SEM techniques and examined the following possibilities: estimation of greater reputational damage mediates a negative relationship between relational mobility and social media addiction (Hypothesis 2), whereas (2) loneliness, extroversion, and motivation to expand social networks mediate a positive relationship between relational mobility and social media addiction (Hypotheses 4a, 4b, and 4c; Fig 1 ). However, the model did not fit the data (RMSEA = 0.24).

In this model, we focused on three factors that would mediate the positive relationship between relational mobility and social media addiction (Hypotheses 4a, 4b, and 4c): loneliness, extroversion, and the motivation to expand the social network. Indeed, extroversion and the motivation to expand the social network were significantly and positively correlated with social media addiction ( r = 0.12, p < 0.01; r = 0.28, p < 0.01), but loneliness was not ( r = 0.04, p = 0.23).

Therefore, we focused only on the motivation to expand the social network, as it had the strongest correlation with social media addiction. We used SEM techniques and examined the following possibilities: (1) estimation of greater reputational damage incurred by ignoring messages mediates the negative relationship between relational mobility and social media addiction (Hypothesis 2) and (2) motivation to expand social network mediates the positive relationship between them (Hypothesis 4c). This model fit the data (RMSEA = 0.05; Fig 2 ). We conducted an additional analysis using age and sex (male = 0, female = 1) as control variables. To examine the effect of sex, we excluded 21 participants who did not answer “male” or “female.” The result was the same as that of the aforementioned analysis (RMSEA = 0.05; Fig 2 ). The effect of sex on social media addiction was statistically significant (β = 0.08, p = 0.01), whereas that of age was not (β = 0.01, p = 0.85).

Note**: p < 0.01; the values in parentheses indicate the results of the additional analyses with covariates (i.e., age and gender).

https://doi.org/10.1371/journal.pone.0300681.g002

Based on these results, we conducted a dual mediation analysis using 1000 bootstrap samples. The results indicated that reputational damage estimation mediated the negative relationship between relational mobility and social media addiction (indirect effect = -0.01, p = 0.03), whereas the motivation to expand social network mediated the positive relationship (indirect effect = 0.04, p < 0.01; Fig 3 ). We also conducted an additional analysis using age and sex (male = 0, female = 1) as control variables, and obtained same result as that in the aforementioned analysis; reputational damage estimation mediated the negative relationship between relational mobility and social media addiction (indirect effect = -0.01, p = 0.03), whereas the motivation to expand social network mediated the positive relationship (indirect effect = 0.04, p < 0.01; Fig 3 ). These results support Hypotheses 2 and 4c.

https://doi.org/10.1371/journal.pone.0300681.g003

When we used extroversion or loneliness instead of motivation to expand the social network, the models did not fit the data (RMSEA = 0.12 and 0.20, respectively). The models did not fit when entering the control variables (age and gender), either (RMSEA = 0.09 and 0.14, respectively).

3.2.2. Do people in low relational mobility societies overestimate their reputational damage?

Next, we examine Hypothesis 3. We conducted a generalized linear mixed model analysis in Study 2, although we conducted a multiple regression analysis in Study 1. This is because Study 2 employed the within-participants design, whereas Study 1 employed the between-participants design.

Prior to the analysis, we constructed a dummy variable for the evaluator (participants’ actual evaluations = 0, evaluation from others = 1). After centering all variables in the model, we used a generalized linear mixed model with random intercepts for participants to examine our hypothetical model. The evaluation was predicted using the dummy evaluator variable, a relational mobility variable, and the interaction between the two.

The main effect of the evaluator was significant (β = 0.20, p < 0.01), but the interaction effect between the evaluator and relational mobility was not (β = 0.01, p = 0.49). These results imply that people in low relational mobility societies estimate that greater reputational damage will be incurred by ignoring messages than the actual damage (consistent with Hypothesis 3), and the same hold true for people in high relational mobility societies (inconsistent with Hypothesis 3).

The main effect of relational mobility was also significant (β = -0.10, p < 0.01), which implies that (1) people in low relational mobility societies evaluated those who ignored messages more negatively than those in high relational mobility societies, and (2) people in low relational mobility societies also estimated that ignoring messages would incur higher reputational damage than those in high relational mobility societies.

We conducted an additional analysis using age and sex (male = 0, female = 1) as control variables and obtained the same results as those in the aforementioned analysis. The main effects of the evaluator (β = 0.20, p < 0.01) and relational mobility (β = -0.10, p < 0.01) were statistically significant, whereas the interaction effect between them was not (β = 0.01, p = 0.50). Additionally, the main effects of age (β = -0.04, p = 0.14) and sex (β = -0.02, p = 0.39) were not significant.

3.2.3. Moderating effect of age.

We exploratively examined the moderating effect of age; whether the effect of reputational damage estimation on social media addiction and that of the motivation to expand the social network differs depending on age. We conducted a multiple regression analysis after centering all variables in the model. Social media addiction was used as the dependent variable, whereas relational mobility, reputational damage estimation, the motivation to expand the social network, sex, age, the interaction between reputational damage estimation and age, and the interaction between the motivation to expand the social network and age as independent variables. The main effects of reputational damage estimation (β = 0.15, p < 0.01) and the motivation to expand the social network (β = 0.30, p < 0.01) were statistically significant, whereas those of relational mobility (β = -0.02, p = 0.42), age (β = 0.00, p = 0.90), and sex (β = 0.05, p = 0.07) were not. Additionally, the interaction effect between reputational damage estimation and age was not statistically significant (β = -0.01, p = 0.70), whereas that between the motivation to expand the social network and age was statistically significant (β = -0.06, p = 0.03). The effect of the motivation to expand the social network was greater among younger participants ( M - 1 SD ; β = 0.36, p < 0.01) than among older ( M + 1 SD ; β = 0.24, p < 0.01).

3.2.4. Discussion.

Study 2 hypothesized that (1) reputational damage estimation mediates the negative relationship between relational mobility and social media addiction, whereas (2) loneliness, extroversion, and the motivation to expand the social network mediate the positive relationship. We hypothesized that these two mediations would cancel each other out; therefore, there will be no relationship between relational mobility and social media addiction.

We tested these hypotheses using SEM; however, they were not supported. We additionally tested another model that focused only on the motivation to expand the social network: (1) reputational damage estimation mediates the negative relationship between relational mobility and social media addiction and (2) motivation to expand the social network mediates the positive relationship. This model was supported, which implies that the factors promoting social media addiction differ between high and low relational mobility societies. Reputational damage estimation strengthens social media addiction in low relational mobility societies, whereas the motivation to expand social networks strengthens it in high relational mobility societies.

3.3. General discussion

3.3.1. summary of results..

We focused on message exchanges on social media and investigated the effect of social environment (relational mobility) on social media addiction. In Study 1, we examined the following hypotheses: (1) people in low relational mobility societies are more strongly addicted to social media (Hypothesis 1), (2) the estimation of greater reputational damage incurred by ignoring messages mediates the negative relationship between relational mobility and social media addiction (Hypothesis 2), and (3) people in low relational mobility societies overestimate the possibility of receiving a bad evaluation, whereas people in high relational mobility societies do not. In Study 2, we additionally examined (4) loneliness, extroversion, and the motivation to expand the social network mediate the positive relationship between relational mobility and social media addiction (Hypotheses 4a, 4b, and 4c).

Hypotheses 1 and 2 were not supported; we conducted a mediation analysis but observed no relationship between relational mobility and social media addiction or between reputational damage estimation and social media addiction. In contrast, when we conducted the correlational analyses, although we found no statistically significant correlation between relational mobility and social media addiction, we found a statistically significant negative correlation between relational mobility and reputational damage estimation, as well as a statistically significant positive correlation between reputational damage estimation and social media addiction. These results are partially consistent with Hypothesis 2 and imply that other factors mediate the positive relationship between relational mobility and social media addiction, which might negate the negative relationship hypothesized in Hypothesis 1.

Therefore, we additionally examined this possibility in Study 2 (Hypotheses 4a–c), which was partially supported: reputational damage estimation mediated the negative relationship between relational mobility and social media addiction, whereas the motivation to expand the social network mediated the positive relationship. This result supports Hypotheses 2 and 4c.

Additionally, we found a statistically significant main effect of sex in both Studies 1 and 2 in that females were more strongly addicted to social media than males were. Chen et al. [ 54 ] demonstrated a difference between sexes in the factors associated with smartphone addiction. They found that females were more likely to be addicted to smartphones as they used social networking services, whereas this relationship was not found for males. Although it is only a speculation, our study demonstrated that females were more strongly addicted to social media, partially because we focused on LINE, a social media especially for connecting with others.

The explorative analysis in Study 2 demonstrated an interesting interaction effect between age and the motivation to expand the social network. Those with a higher motivation to expand the social network were more strongly addicted to social media, and this effect was smaller among older people ( M + 1 SD ) than younger ones ( M - 1 SD ). This may be partially because social media usage does not expand the social networks among older people as much as among younger people. According to Kojima (2022) [ 55 ], the rate of those who use LINE every day was lower among older people (50s male: 56.3%; 50s female: 69.9%) than among young people (20s male: 76.2%; 20s female: 86.8%). Even when older people try to send messages to their friends through social media, their friends may not use social media. Future research should focus on the differences in the social media environments between various age groups.

In contrast to Hypothesis 4a, loneliness did not mediate a positive relationship between relational mobility and social media addiction. We found no relationship between loneliness and social media addiction ( r = 0.04, p = 0.23). This result is inconsistent with previous studies that have demonstrated a positive relationship between loneliness and social media addiction [ 23 ]. This non-significant relationship was surprising in that the COVID-19 pandemic would have strengthened the relationship between loneliness and social media use. Kayis et al. [ 56 ] demonstrated that the fear of COVID-19 strengthened loneliness, which in turn strengthened smartphone addiction. Although speculative, the capacity to be alone might have weakened the relationship between loneliness and addiction. As the capacity to be alone is negatively related to social media addiction [ 57 ], even when individuals feel lonely, if their capacity to be alone is significant, they will not be strongly addicted to social media. We also found that loneliness was significantly negatively correlated with relational mobility ( r = -0.26, p < 0.01), which is inconsistent with the results obtained by Oishi et al. [ 50 ], who found that participants felt lonelier when they were asked to imagine a situation wherein they frequently moved to a different location. Although relational mobility is high in societies in which people move frequently [ 58 ], this is not always the case. Even in such societies, some people have fewer opportunities to construct new relationships. This might be the reason that our results, which focused on relational mobility, were inconsistent with those obtained by Oishi et al. [ 50 ].

Moreover, in contrast to Hypothesis 4b, when we used SEM techniques and examined the following hypotheses, the model did not fit the data (RMSEA = 0.12). Although the model did not fit the data, the direction of each path was statistically significant and consistent with our hypotheses: (1) people in a lower relational mobility society estimated greater reputational damage (β = -0.21, p < 0.01), which strengthened their social media addiction (β = 0.14, p < 0.01), whereas (2) people in a higher relational mobility society were more extraverted (β = 0.40, p < 0.01), which strengthened their social media addiction (β = 0.12, p < 0.01). A reason why our model did not fit the data was the weak relationship between extroversion and social media addiction. Although some studies have demonstrated a positive relationship between extroversion and social media addiction [ 17 ], others have indicated no relationship between them [ 15 ]. Future studies should examine the factors that moderate the relationship between extroversion and social media addiction.

We also found no relationship between age and addiction in this study. This may be because, compared with studies that have focused on university students [ 28 , 31 , 54 ], the percentage of younger participants in our studies was low. Studies 1 and 2 included 6.84 and 6.20% of individuals in their 20s, respectively, and 18.10 and 17% in their 30s, respectively. Additionally, we did not recruit minors (aged < 18 years). This may be a reason for us not finding a relationship between age and social media addiction.

We also examined the accuracy of reputational damage estimation incurred by ignoring messages, as hypothesized in Hypothesis 3, which was partially supported: people in low relational mobility societies overestimate the reputational damage incurred by ignoring messages. This was consistent with Hypothesis 3. Meanwhile, people in high relational mobility societies also overestimated the reputational damage incurred by ignoring messages, which was inconsistent with Hypothesis 3.

3.3.2. Practical implications.

Our results suggest that the antecedent factors of social media addiction differ between high and low relational mobility societies, which implies that interventions for moderating social media addiction differ between high and low relational mobility societies. We demonstrated that people in higher relational mobility societies had a higher motivation to expand social networks, which strengthened their social media addiction. Therefore, moderating this motivation could be effective in preventing social media addiction.

In contrast, we also demonstrated that people in lower relational mobility societies estimated greater reputational damage incurred by ignoring messages, which strengthened their social media addiction. This reputational damage was overestimated. Other relevant studies have found that people who are highly sensitive to rejection are more likely to perceive others’ ambiguous behaviors as intentional rejections [ 59 ] and are more strongly addicted to social media [ 60 ]. These studies, as well as our results, imply that people overestimate the possibility of being rejected or reputational damage incurred by ignoring messages, which can strengthen their social media addiction. Therefore, correcting this estimation can be effective for lowering social media addiction, especially among those in lower relational mobility societies or those more sensitive to rejection.

One specific intervention can be to provide them with feedback that ignoring messages does not lower their reputation as much as they estimate. For example, by asking students in a class to evaluate those who ignore messages on social media and by showing them the distribution or average of the evaluation score, they would notice that they overestimate the reputational damage. This type of intervention has previously succeeded in changing behavior, such as reducing college students’ alcohol consumption [ 61 ]. Students used to excessively consume alcohol based on the incorrect estimation that other students prefer alcohol [ 62 ], but by correcting their inaccurate estimation (i.e., by informing them that other students do not prefer alcohol as much as they estimated), their alcohol consumption was decreased [ 61 ]. Although these studies were conducted 30 years ago and did not involve social media, we believe that modifying or correcting the estimation of reputational damage can be a novel and important intervention to lower addiction as it focuses on interpersonal miscommunications between social media users, which differs from other interventions that focus on individual aspects (e.g., asking users to reflect on “what social media they used, how long and how they used the social media, their thoughts and emotions related to their social media use” [ 5 ]).

3.3.3. Limitations and future work.

This study had several limitations. First, it only included participants from Japan. We assumed that even in Japan, different cities have different degrees of relational mobility. Indeed, some studies have surveyed people in Japan and demonstrated the effect of relational mobility on their attitudes [ 63 , 64 ]. However, Japanese people do not necessarily live in high relational mobility societies because relational mobility in Japan is low [ 44 ]. This might be the reason that, inconsistent with the prediction of Hypothesis 3, people from both high and low relational mobility societies overestimated the possibility of earning a bad reputation by ignoring messages. Additional studies must be conducted in higher relational mobility societies, such as the United States, to investigate our hypotheses.

Second, we used a crowdsourcing service to recruit participants from both high and low relational mobility societies. However, it has been demonstrated that some participants recruited via crowdsourcing services answer questions carelessly [ 65 ], which might have affected our results. Although we performed an instructional manipulation check and excluded those who did not pass it, additional studies are required to ensure the robustness of our results.

Finally, our study did not sufficiently investigate the effects of COVID-19 on distress or social media addiction. Some studies have focused on the psychological distress caused by the COVID-19 pandemic and demonstrated that it strengthened social media addiction [ 20 , 30 ]. Therefore, a possible intervention for moderating social media addiction is to lower psychological distress. Tambelli et al . [ 66 ] surveyed late adolescents (aged between 18 and 25 years) and demonstrated that those who felt a greater sense of security from their parents or peers exhibited lower COVID-19-related distress. Lowering distress by constructing good relationships with parents or peers could weaken social media addiction, at least among late adolescents.

4. Conclusion

This study demonstrated that the antecedents of social media addiction differ between high and low relational mobility societies. In Study 1, we demonstrated that people in low relational mobility societies estimate greater reputational damage, but there was no direct relationship between relational mobility and social media addiction. Therefore, in Study 2, we additionally explored the factors that mediate the positive relationship between relational mobility and social media addiction. The results indicated that (1) people in lower relational mobility societies expect higher reputational damage, which strengthens their social media addiction; and (2) people in high relational mobility societies are more motivated to expand their social networks, which strengthens their social media addiction. In addition, both studies demonstrated that people expect greater reputational damage than the actual damage. These results imply that the mechanism of social media addiction differs depending on the social environment: the estimation of reputational damage strengthens social media addiction in low relational mobility societies, whereas the motivation to expand social networks increases social media addiction in high relational mobility societies. Therefore, correcting this damage overestimation would be an effective strategy to moderate social media addiction, especially in low relational mobility societies, whereas reducing the motivation to expand social networks would be effective especially in high relational mobility societies.

• View Article
• Google Scholar
• 2. Zou Z, Wang H, d’Oleire Uquillas F, Wang X, Ding J, Chen H. Definition of Substance and Non-substance Addiction. In: Zhang X, Shi J, Tao R, editors. Substance and Non-substance Addiction. Singapore: Springer; 2017. pp. 21–41. https://doi.org/10.1007/978-981-10-5562-1_2
• PubMed/NCBI
• 13. American Psychiatric Association. Diagnostic and statistical manual of mental disorders: DSM-5-TR. 5th ed., text revision. Washington, DC: American Psychiatric Association Publishing; 2022.
• 46. ICT Research & Consulting Inc. 2022. Survey of SNS usage trends in 2022. ICT Research & Consulting Inc. 2022 May 17 [Cited 2023 July 22]. Available from: https://ictr.co.jp/report/20220517-2.html/
• 58. Oishi S, Schug J, Yuki M, Axt J. The Psychology of Residential and Relational Mobilities. In: Gelfand MJ, Chiu C, Hong Y, editors. Handbook of Advances in Culture and Psychology, Volume 5. Oxford University Press; 2015. p. 221. https://doi.org/10.1093/acprof:oso/9780190218966.003.0005

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Hooked on virtual social life. Problematic social media use and associations with mental distress and addictive disorders

Vincent henzel.

1 Faculty of Medicine, Dept of Clinical Sciences Lund, Psychiatry, Lund University, Lund, Sweden

2 Malmö Addiction Center, Region Skåne, Malmö, Sweden

Anders Håkansson

Associated data.

The data set cannot automatically be shared publicly, because that is not consistent with the data handling statement made in the original ethics application. These restrictions have been imposed by the Swedish Ethical Review Authority, Uppsala, Sweden. Requests for data access would have to be formally reviewed by the ethics review board. Data access requests may be directed to the Swedish Ethical Review Authority (contact via es.gninvorpkite@rotartsiger ), to the Corresponding Author (contact via [email protected]_sredna ), or to Åsa Westrin (contact via [email protected] ).

Social media is an important and growing part of the lives of the vast majority of the global population, especially in the young. Although still a young and scarce subject, research has revealed that social media has addictive potential. The aim of this cross-sectional study was to explore the associations between problematic use of social media and mental distress, problematic gaming and gambling, within the Swedish general population.

Data from 2,118 respondents was collected through self-report questions on demographics and validated scales measuring addiction-like experiences of social media, problem gaming, problem gambling, and mental distress. Associations were analyzed in unadjusted analyses and–for variables not exceedingly inter-correlated–in adjusted logistic regression analyses.

In adjusted analyses, problematic use of social media demonstrated a relationship with younger age, time using instant messaging services, and mental distress, but not with education level, occupational status, or with treatment needs for alcohol or drug problems. Behavioral addictions (internet, gaming and gambling) were substantially inter-correlated, and all were associated with problematic use of social media in unadjusted analyses.

Conclusions

Social media use is associated with other addictive behaviors and mental distress. While factors of causality remain to be studied, these insights can motivate healthcare professionals to assess social media habits, for example in individuals suffering from issues concerning gambling, gaming or mental health.

Introduction

There is a growing body of research indicating that using modern technologies, such as the Internet, video games, smart phones and social media platforms, has the potential of being addictive [ 1 – 3 ]. Social media addiction is not yet an established diagnosis, although it is one of many suggested behavioral addictions [ 4 ]. Among these, the gambling disorder is the only one recognized in the Diagnostic and Statistical Manual for Mental Disorders, while Internet gaming disorder is listed in its appendix as a condition which requires further research [ 5 ].

Worldwide, the number of people with an active Facebook account is predicted to surpass three billion [ 6 ] in the year 2021. Other popular platforms such as YouTube, Snapchat, and Instagram are also attracting an increasing number of users, especially from younger populations [ 7 ]. Constant online presence and availability has become something of a status quo in the lives of the vast majority, made possible with the rise of highly accessible and user-friendly laptops and smartphones. Using social media is one of the most common activities on the Internet [ 8 ], with daily use reported by nine out of ten youths worldwide [ 9 ]. Social media presents users with a broad spectrum of activities, ranging from maintenance of real life relationships through chats and calls, sharing one’s own or others’ creative content and opinions, partaking in communities, playing games, gambling, and passing time looking through the activities of other users [ 3 ]. This diversity of activities is a scientific challenge concerning whether social media can be considered addictive as a whole, or if it is rather a question of which parts of it that are potentially negative, as well as beneficial for the individual.

Academic work in this field has been criticized for its inconsistencies of defining normal and problematic usage, as well as using non-standardized measurement tools, subsequently rendering comparison of results and prevalence rates difficult. Confusion also surrounds what social media actually entails, and whether it is synonymous to social networking or not, as it is sometimes referred as [ 4 , 10 ]. In fact, the existent literature is inconsistent on labeling this issue; social media addiction, excessive, problematic, and at-risk use are applied both separately and homogeneously. Moreover, online activities such as sharing photos, engaging in communities, and communicating with real life friends can be associated with lower degree of loneliness and less psychological distress [ 11 , 12 ]. These particular activities are also believed to be mediators of shaping and maintaining the user’s sense of identity and belonging, safety and competence, satisfying basic psychological needs [ 13 ], as well as bringing higher psychosocial wellbeing, which apparently is the case particularly for young users [ 14 – 16 ].

Studies have demonstrated that interrelationships between different behavioral addictions exist, suggesting common underlying risk factors, together with associations with addictive use of psychoactive substances [ 17 ]. This has bred a widely used matrix of characteristics, first proposed by Griffiths as a ‘components model of addiction’ [ 18 ], involving a number of key components; salience (typically defined as the preoccupation in thoughts, and the vast amount of time and energy spent on either thinking of or carrying out the behavior), mood modification, tolerance, withdrawal, loss of control, and conflicts [ 18 , 19 ]. Some authors [ 20 , 21 ] have proposed socio-cognitive mechanisms for how Internet-related addictions develop. These revolve around negative beliefs about the self and the world, with thoughts such as ‘I’m nobody offline, but online I am someone’ or ‘the online world is safer than the real one’. This could reasonably lead to a preference for online interaction, an overreliance to the services provided by the Internet and social media, thus giving rise to an addictive state for the individual. With the component model as background, problematic use of social media has been suggested to consist of an exaggerated concern about social media, a strong motivation to use it, and the devotion of time and effort to social media to the extent that it negatively impacts on other social activities, studies, work, interpersonal relationships, and/or mental health and well-being [ 22 ].

There is a lack of reviews on the subject, and agreed-upon definitions or gold standard measurements and cutoffs have not been established. One part of this research gap is that–apart from differing definitions–prevalence rates reported also derive from different populations and in very different settings; prevalence figures reported have differed widely from a low of 1.6% in a study carried out in Nigeria, and a high of 34% in a study from China. When including general Internet addiction as well as Facebook addiction specifically, rates of 2–12% have been reported from various literature reviews [ 23 – 25 ]. Even though anyone with access to social media can become addicted to it, this holds true especially for young people, a group that has been increasingly exposed to technology, and consistently reported to use and abuse social media to a larger degree than adults [ 26 ]. From Europe and the US [ 27 , 28 ], recent studies and reports have found high rates of technology use among the young. A large proportion of children as young as 8 months use a screen daily, with the majority of 11-year-olds owning a smartphone, and 8-12-year-olds using screens for entertainment purposes almost five hours per day.

There are also associations to being single, as well as to low education level and lower monthly income, and although evidence is somewhat inconclusive for gender differences, several findings point to a slight overrepresentation among female users [ 29 – 31 ]. Suffering from mental disorders such as depression and anxiety has been long known to increase the risk for, and be exacerbated by, behavioral and substance addictions alike [ 32 , 33 ]. Numerous studies, including comprehensive reviews, have shown that addictive use of social media is associated with depression, anxiety and mental distress [ 26 , 34 – 37 ].

There have been discoveries regarding associations and interrelationships between addictive online behavior and use of psychoactive substances, in a number of European countries. Problematic use of video games and social media have displayed a small but significant relationship [ 23 ], while multidirectional associations between problematic gaming, gambling, and internet use have been observed as well [ 38 ]. Excessive use of the Internet has been shown to increase likelihood for use [ 39 – 42 ] and abuse [ 43 , 44 ] of alcohol and other substances. A relationship between time spent on social media and episodic heavy drinking has also been found [ 45 ].

There is a scarcity of studies on prevention or treatment for technology-related addictions. Mindfulness techniques [ 46 ], and psychotherapeutic interventions, have shown improvement of symptoms in some patients, but more controlled trials are needed to develop any standardized treatment regimens [ 47 – 49 ]. Thus, altogether, the overall understanding of addictive social media behavior is hitherto limited, with respect to topics ranging from risk factors and prevalence to the clinical picture and treatment possibilities. Based on this research gap, more knowledge is needed in the basic understanding of the existence and correlates of this construct in the population.

Altogether, based on the relative novelty of research in the area of social media addiction, large research gaps remain in the area. For example, considerably more information is needed about the correlates of problematic social media use, both in the population as a whole and in younger individuals specifically. Also, given the uncertainty so far about the definition of social media, there is reason to study if and how the present construct is association with other behavioral addictions often related to online platforms, such as gaming, gambling and the overall use of the internet. In addition, it is of importance to study and to control for the time spent in typically occurring instant messaging services, when also considering other types of possible risk factors.

Thus, the present study aimed to assess problematic use of social media as the primary outcome of the study, and exemplifying it mentioning some brand names representing the most common social media reported (Facebook and Instagram) [ 50 ], as well as one of the social media somewhat less common but often referred to in the media (Twitter). Also, the study aimed to outline the possible correlations of problematic social media use with demographics, mental distress, and other behavioral addictions, in the general population as a whole, as well as in the sub-population of adolescents/younger adults. The present study hypothesized that addictive social media behavior, in the general population, may be related to symptoms of psychological distress, and to a history of treatment needs for problematic alcohol or drug use.

Study design

A cross-sectional self-reported online survey design was utilized, targeting the general Swedish population. The questionnaire assessed a spectrum of behavioral addictions, of which problematic use of social media was the main outcome variable. In the present analyses, problematic social media use was treated as the outcome variable, testing it against a number of factors suggested in the literature to be associated with this behavior.

Participants and procedures

Data collection from a general Swedish population sample was conducted with the help of a marketing survey company (Userneeds AB), who administered a self-report online questionnaire via e-mail to their web panel members in six age groups ranging from 16 to 80 years, aiming for a nationally representative sample. Potential participants included members of the web panel of Userneeds, who typically receive offers to participate in market surveys for commercial products and similar. Each completed survey grants the respondent a reward of approximately 1€.

Electronic written consent was required in order for the questionnaire to open. To ensure anonymity and confidentiality, the IP-address of each respondent was hidden from the researchers. To complete and thereby send the filled questionnaire to the researchers, every single question had to be answered, and though they could not be skipped, some items had an optional answer reading “do not wish to answer”. The target was to reach a sample of 2000 individuals, equally divided regarding gender, and stratified by age. The full sample was reached in October, 2019, totaling 2118 partial and 2002 completed surveys, ending four weeks of data collection.

The National Ethical Review Board, Sweden, approved the study in August, 2019, and stated it was not subject to Swedish ethics legislation as it does not involved identified personal data. The present study is based on data from the same overall online survey data collection as a different scientific publication in a separate line of research, a paper assessing history of voluntary self-exclusion in gambling through a novel multi-operator self-exclusion service in the present setting [ 51 ].

Instruments and measures

For the primary outcome measure of social media addiction, the study used the Bergen Social Media Addiction Scale (BSMAS) includes six Likert scale items, graded 1–5 (‘Never’–‘Very often’) about the following experiences, during the last 12 months: spending a lot of time thinking of social media or planning what to do there; desiring to use social media more and more; using social media to forget about personal issues; tried to cut down use without success; becomes restless or anxious when unable to access social media; used social media to such a degree that it has impacted your work or studies negatively. Scoring ranges from 6–30, with 19 or above indicating problematic use of social media, and the scale has been validated and shown to have a Cronbach alpha of 0.86 [ 52 , 53 ]. As in recent years, the authors behind the present scale have recommended a score of 19 as the cut-off indicating problematic social media use [ 52 – 54 ], the present study used a dichotomous classification of respondents as either above or below cut-off, rather than using the instrument in a dimensional way.

The start of the social media item was that the questions were to deal with the respondent’s use of social media (Facebook, Twitter, Instagram and similar) during the past 12 months. The social media given as examples here, as well as the ‘and similar’ wording, were provided in order to mention the most common ones, i.e. Facebook and Instagram, as well as Twitter as an example of a service also being relatively common but which may also appear in somewhat different contexts, such as in reports in the professional life and in business and traditional media [ 50 ].

In order to study potential correlates of social media addiction, related to problematic internet use, problem gambling, problem gaming and psychological distress, the study used the following screening tools:

• The Problematic and Risky Internet Use Screening Scale-3 (PRIUSS), is made up of three items grading Internet behavior with a five-point Likert scale ranging from never to very often. This scale has no time frame, rather it assesses current symptoms. The questions evaluated to what degree the subject had experienced increased anxiety because of Internet use, felt anxious when not using the Internet, and how often they felt a loss of motivation for more important tasks. Score range is 0–12, with a cutoff at 9 points for risky Internet use [ 55 ]. Cronbach-alpha value has been reported to be 0.81.
• The Gaming Addiction Scale (GAS), also a five-point Likert scale ranging from 1–5 (‘Never’ to ‘Very often’), was developed and validated by Lemmens et. al [ 56 ]. It begins with the phrase ‘How often during the last six months…’, followed by seven questions: did you think about playing a game all day long; did you spend an increasing amount of time on games; did you play games to forget about real life; have others unsuccessfully tried to reduce your game use; have you felt bad when you were unable to play; did you have fights with others (e.g., family, friends) over your time spent on games, and; have you neglected other important activities(work, school, exercise) to play games. The cutoff for at-risk gaming behavior is 21 out of a total of 35 points, and the internal validity of this tool has repeatedly demonstrated a Cronbach-alpha above 0.7 [ 57 ].
• The Problem Gambling Severity Index (PGSI), developed in Canada [ 58 ], consists of nine items corresponding to the diagnostic criteria for Gambling Disorder in the DSM-5. Assessing gambling behavior during the recent twelve month period, the scale grades loss of control, increased urges, returning to reclaim losses, whether they had borrowed money or sold something to gamble, self-awareness of problematic gambling, receiving criticism from others, feelings of guilt, gambling had caused any health issues, and lastly any financial problems. In this research paper, which does not seek to screen for or subdivide gambling behavior, scoring 9 or higher out of 27 possible points is regarded problematic [ 59 , 60 ]. The Cronbach-alpha has been shown to be 0.77 [ 60 ].
• The Kessler Psychological Distress Scale (Kessler-6), a six-item questionnaire with a 5-point Likert scale on each one (‘Not at all’ to ‘Almost constantly’), assessing non-specific mental distress during the past six months. It is widely used both clinically and academically as a tool for severity or screening [ 61 ]. With questions inspired from the symptomatology of depression and anxiety, it asks how often the respondent had been nervous, restless or fidgety, so depressed that nothing could cheer him/her up, felt that everything was an effort, and had felt worthlessness. A cut-off at 13 out of 24 possible points indicates severe mental distress. It includes an option to answer ‘Do not wish to answer’ on each item, although these were excluded from statistical analyses. The Cronbach-alpha of the Kessler-6 scale has been reported to be 0.89 [ 61 ]

In addition, study variables included sociodemographic data; age (in age groups), gender (male, female, or do not wish to answer), marital status, occupational status, level of education, and monthly income in discrete intervals. The five categories of occupational status were merged into two categories; working (59.8%), or not working (40.2%) which covered studying, seeking employment, being retired, or being on sick leave ( Table 1 ).

All individuals included (N = 2,002).

Given the previous literature on likely associations between social media behavior and substance use, the study included items describing problematic substance use. These variables included one dichotomous item about whether the respondent had ever felt the need to seek treatment for alcohol problems, and one variable about having felt the need to seek treatment for drug problems (defining drugs as illicit drugs and addictive prescription drugs such as prescription sedatives or strong analgesics). Also, a corresponding item assessed whether respondents had ever felt the need to seek treatment for mental distress. These items included the possibility to refuse to answer. Moreover, the quantity of time spent on online messaging services was included, with examples of such use being instant messaging services such as Facebook Messenger, Instagram Direct Messaging, as well as WhatsApp and regular phone texting. Here, options ranged from below one hour daily, to more than four hours daily ( Table 1 ).

Statistical methods

SPSS was used for statistical observations and analyses. A total of 2118 questionnaires were initiated, of which 116 were excluded because of incomplete status. Subsequently, descriptive characteristics were observed, and the primary outcome was characterized as problematic or non-problematic use of social media. In a first univariate comparison of respondents with problematic or non-problematic social media use, these groups were compared using the chi-square test for categorical data, and the Mann-Whitney U-test for continuous variables. Thereafter, in order to adjust variables for one another, logistic regression analyses were performed.

In a binary correlation matrix run for each pairwise combination of variables, some variables displayed relatively pronounced correlations; the association reaching the highest level of correlation (a Pearson correlation of 0.65) was between gaming (GAS) and gambling (PGSI), and three further associations were above a correlation of 0.50 (PRIUSS and GAS, 0.56, the Kessler score and having felt a need to seek treatment for mental health, 0.54, and PRIUSS and the Kessler score, 0.53). Time on instant messaging services and PRIUSS had a correlation of 0.43, and PRIUSS and PGSI 0.41, age and GAS 0.40, age and the Kessler score 0.40, and GAS and time on instant messaging 0.40, whereas all other correlations were below 0.40. Thus, in the logistic regression, data for behavioral addictions (PRIUSS, GAS and PGSI) were not assessed, due to the high statistical overlap between them, and these were therefore only reported in univariate analyses. Finally, in addition, due to the close correlation (and conceptual similarity) between the Kessler-6 score and the item describing the need to seek help for mental health, only the Kessler-6 score was included in the logistic regression. Finally, these regression analyses therefore included age group, gender, occupational status (working vs not working), level of education, number of hours spent in instant messaging services, the Kessler-6 score for mental health, and ever having felt a need to seek treatment for alcohol problems, and for drug problems, respectively). The adjusted logistic regression analyses carried out included one for the whole sample (n = 1,863, after exclusion of 139 individuals with missing data for any of the variables assessed), and one including only the youngest age groups (>40 years, n = 677, after exclusion of 59 cases with missing data).

Descriptive observations

The number of initiated questionnaires was 2118, of which 116 were not completed and thus not included in statistical analysis. Of the remaining 2002 entries, demographics were distributed almost evenly regarding gender (1009 female, 989 male). Age distribution was skewed with the youngest groups being in minority compared to the older ones ( Table 1 ). Educational level was split into five categories, with distribution percentages (%) presenting at 5.5 for primary school, 34.7 for high school, 16.6 for an incomplete university degree, 38.7 for a complete degree, and lastly 4.4 per cent for the category labeled ‘Other’. Having experienced a need for help seeking was reported by 36.6, 4.2 and 2.1 percent for mental distress, alcohol problems, and drug problems, respectively.

Five percent of the total sample reached scores of 19 or above of the BSMAS, indicating problematic use of social media. Of the six symptomatic components of the BSMAS scale, the most common complaint in the sample was salience (18.1%), the least common being usage leading to negative consequences (3.8%). A majority of individuals spent up to two hours (83.4%) communicating with instant messaging services, with 16.6 percent spending two hours or more. The medians for the PRIUSS, GAS, PGSI, and Kessler-6, were 2, 7, 0, and 4 respectively. A history of having felt the need to seek help for mental distress, and alcohol and drug use, were reported by 37, four and two percent, respectively.

Associations with problematic use of social media

The univariate analysis showed no significant difference between men and women in regards to problematic use of social media, while there was a clear difference across age groups, with the highest percentages of problem users found in the younger groups (16–39 years), and a steep decrease was observed in the two older groups (40 years and above). Working or not revealed no significant difference, while educational level did, with individuals finishing high school having the highest percentage of social media addiction (7.2%), while the category of ‘other’ education showed the lowest (1.1%). Time spent chatting, as well as medians for each psychometric scale measured, were significantly associated with problematic use of social media ( Table 2 ).

Continuous variables, i.e the scales, were tested for significance through a Mann-Whitney U analysis, while categorical data were treated with chi-square testing. The 25 th to 75 th percentile signify the interquartile range.

In logistic regression, age displayed a negative associations with problematic social media use (OR 0.66 [0.55–0.78]), whereas the level of time spent in instant messaging services (OR 2.15 [1.79–2.58]), as well as the Kessler-6 score (OR 1.11 [1.05–1.17]), were positively associated with problematic social media use. Occupational status, level of education, as well as having felt a need to seek help for alcohol and drug use were not significantly correlated to the outcome ( Table 3 ). In the second logistic regression, including only respondents below 40 years of age, the association with age was no longer statistically significant, whereas the outcome variable remained significantly associated with time using instant messaging services (OR 2.03 [1.65–2.49]), and with the Kessler-6 score (OR 1.09 [1.03–1.16], Table 4 ).

Analyses after exclusion of 139 cases with non-complete for included variables.

*p<0.05.

**Education level was analyzed covariately within the variable, omitting the category ‘other’ to rank the different education types.

Age groups 39 years and younger (N = 677). Analyses after exclusion of 59 individuals with incomplete data for included variables.

Among measures of behavioral addiction, the problematic social media group had markedly higher scores for both problem internet use (PRIUSS, p<0.001), problem gaming (GAS, p<0.001), and problem gambling (PGSI, p<0.001). In addition, individuals with problematic social media use were markedly more likely to score above cut-off for problem internet use (29% vs 1% in the non-problem social media group, p<0.001), problem gaming (38% vs 3%, p<0.001), and problem gambling (56% vs 5%, p<0.001). Problem gambling, problem gaming and problem internet use were closely linked to one another; the risk of meeting established criteria of problem gambling was markedly increased with an increasing GAS score (OR 1.45 [1.38–1.52], p<0.001), or an increasing PRIUSS score (OR 1.73 [1.59–1.88], p<0.001).

In the present study, using a web survey addressing the general population, problematic use of social media was significantly associated with younger age, time using instant messaging, and general mental distress, and, in unadjusted analyses, also with each of the behavioral addictions including problem internet use, problematic gaming, and problematic gambling,. No independent associations were found for gender, educational level, occupational status, or having felt a need to seek help for drug or alcohol problems. The is one of few studies hitherto examining the symptoms of problematic social media use, and its correlates, in the population.

Gender did not turn out to be an independent correlate of problematic social media use. The lack of an association with female gender, when adjusting for a number of variables, is in contrast to the findings of previous studies [ 3 , 31 ], although one comprehensive review [ 1 ] gathered a few studies demonstrating an association with male gender, or no association to gender at all. Speculations on the tendency of female over-representation often refer to what type of online activities are engaged in by the two genders. Women spend more Internet time on social media [ 30 ], but tend to use it for mainly for communication purposes and maintenance of already established real life relationships [ 23 ]. Conversely, results show that even though they too spend a lot of time on social media sites, men use the Internet more for gaming or gathering information compared to women, and when they engage in social media, they use it for forming new relationships and seeking communities with similar interests [ 23 , 62 ].

Age was inversely correlated with problematic use of social media addiction, an observation supported by previous research [ 1 , 3 , 23 ]. This unsurprising find might be derived from several factors. In contrast to the childhoods of older generations, children today are increasingly exposed to technology during their formative years, at an age that seems to be steadily decreasing [ 27 , 28 ]. They are taught in schools and at home how to handle technologies otherwise regarded as complex to introduce to older people, who rely more on traditional means of communication, as well as managing work and everyday life.

As children grow into teen age, they can experience increased peer pressure and a stronger need to achieve a sense of community, belonging and identity [ 15 ], which can evidently be satisfied by the various services of social media [ 3 ]. Furthermore, the fear of missing out has been proposed as another motivator for youths to constantly check their social media applications, adding to the need of constant online presence [ 63 ]. Although it remains to be seen whether higher age will be a protective factor for the addictive use of technology in the future, this current finding can be considered expected and in line with previous literature. However, despite the overall idea that problematic social media behavior may be more pronounced in the young, in the present sub-analysis of younger individuals only, the same correlates of problematic social media use remained statistically significant, except that within this narrower age group, age itself was no longer a significant correlate of the outcome variable.

Our data shows that educational level was not related to problematic use of social media, where all education categories, including the alternative ‘Other’, were separately tested against the first category, i.e. primary school. Andreasen and colleagues [ 23 ] demonstrated a relationship with lower level of education, but argued that it might be due to age rather than the education level itself. Despite the categorical features of the variable used here, and the weakness of a non-negligible number of respondents answering the alternative ‘other’, the results can be regarded as consistent with the findings of Andreasen and colleagues, although more research may be needed in order to fully shed light on the potential link between level of education and social media use. Moreover, our results showed no associations between problematic social media use and occupational status. To the researchers’ knowledge, this is an under-researched area, and findings may so far be hard to interpret.

Our study explored the relationship of problematic use of social media in relation to three commonly discussed non-substance-related addictive behaviors; problematic internet use, problem gaming, and problem gaming. Here, the analyses of the present paper are limited by the fact that these variables, between one another, tended to be too closely correlated for them to fit into the same logistic regression analysis. In particular, this holds true for the link between gambling and gaming, which displayed a relatively high degree of inter-correlation. However, it was evident that the outcome measure assessed in the present study was closely related to each of these three behavioral addictions.

For example, problematic use of social media was closely associated with the score of the Gaming Addiction Scale. In a similarly designed study, Karlsson and colleagues showed that there was a relationship between problematic gaming and addictive use of the Internet as a whole, measured by the GAS and PRIUSS scales [ 38 ], as in the present study. Despite not explicitly measuring use of social media, those results are somewhat comparable to the ones of this paper, since visiting social media sites is highly common while online [ 8 ]. Gaming and social networking may be rewarding to people in partly the same way; both channels share a potential to provide users with instant gratifications, a sense of purpose and identity, as well as satisfying social needs through online interaction [ 64 ]. Players can instantly message and talk with one another, as well as creating and being part of communities, motivating some players to use games primarily for social fulfillment. Also, probably, both gaming and social networking may even fill a purpose of personal or professional development, such as through a sports career in e-sports, or the professional and commercial value of marketing in both sectors. For example, the use of social media for professional development may apply to sectors as diverse as health professionals [ 65 ], or even information or disinformation in public health issues [ 66 ]. Likewise, gaming may range from a recreational habit to a professional career path, such as in e-sports [ 67 ]. Adding to the question of gender differences, research has shown that young men use social media sites to play games provided there, sometimes in excess, possibly making the boundaries between risky social media behavior and problematic gaming less obvious.

In addition, the present study indicated that problematic use of social media and problem gambling may correlate with one another. To the researchers’ knowledge, this is the first study assessing this relationship, thus making comparison with previous results somewhat challenging. There are however studies indicating a relationship between gambling and other technological addictions such as problematic mobile phone use, gaming and general Internet addiction [ 38 , 68 ]. Some scholars have noticed similarities in design between certain aspects of casinos and social media sites. Many gambling types deliver rewards at variable ratios, a psychological mechanism notorious for being the most reinforcing type of conditioning, therefore regarded as highly addictive [ 69 ]. An example of this is slot machines, which deliver rewards at irregular intervals, making gamblers unknowing of when to expect cash payback, but expecting it nonetheless. The same can be said about some aspects of technology. The buzz of the smartphone in one’s pocket could be rewarding, a Facebook notification could be a like or a reply to a comment, but is usually something irrelevant. Together with infinite scroll, a function automatically loading more content, these functions may contribute to the addictive mechanisms of social media.

With both problematic gaming and gambling being associated with problematic use of social media, this study indicates that behavioral addictions are connected. It further supports the idea that some individuals might have a general tendency, though not always willingly or voluntarily, to do things in excess and suffer as a consequence.

Although not explicitly asking whether the individual has symptoms of addiction, the questions on having felt the need of seeking help for alcohol or drug use showed no associations with problematic use of social media. These observations may be seen as somewhat surprising, as other studies [ 40 – 42 , 45 ] have reported associations between misusing these substances with regular and the problematic use of smartphones, the Internet and social media, findings supporting the general idea of an interrelationship between addictive conditions. The present study’s variables were brief, in the context of a web survey, and could therefore, at best, be considered a brief screening for substance use problems. Still, however, more research may be needed in order to highlight whether a problematic social media behavior may demonstrate an unexpectedly negative association with more traditional types of addictive behaviors, and importantly, this will require more in-depth screening or diagnostic instruments.

Even though addictions and mental distress are often seen together, it cannot be said with certainty where one issue starts and the other one ends. They may be different in their expressions, but could they share some core origin, a primal human drive for a sense of purpose and belonging? Addressing problematic social media use as a facet of mental health issues could help health care professionals treat their patients synergistically from multiple angles. For example, while an association was demonstrated between problematic social media use and poor mental health, this association does not imply causality. As a large review on Facebook-related depression studies demonstrated, the strongest predictor of depression was not the time spent on Facebook or how frequently it was checked for updates, but rather how much the user compared their life situation with the appearances and activities of others [ 70 ]. This could possibly create and strengthen any perceived social and cultural pressure to be happy and successful, bringing more shame over one’s cognitions, emotions and behaviors.

Moreover, it can be used to strengthen the screening for problematic social media habits in clinical settings where patients with mental health problems are assessed, and likewise, in case of an assessment for an exaggerated social media use, an individual’s mental health should be assessed. Thus, even while demonstrating only cross-sectional associations without further evidence of causality, there may be implications of relevance for routine clinical settings.

Five percent of our total sample had a score of 19 or higher on the Bergen Social Media Addiction Scale, indicating problematic social media use. One interesting finding was the distribution of component symptomatology of social media use. By observing what proportions of our sample answered ‘Often’ or ‘Very often’ on any item, we saw that 18.1 percent were highly preoccupied with social media. This might be affected by several factors, one being that scales measuring addictions generally go from mild to severe experiences, where sole preoccupation is rarely seen without any other symptoms or experiences in addictions. Another factor could be that social needs are increasingly fulfilled online, and just as real-life social situations, people go about their days thinking a lot about what they said to somebody or what they are planning to do with friends, without substantial complaints. Adding to this, the question was phrased as how much time one spent thinking about social media, not how much they worried about it.

The uneven proportion of social media related experiences might indicate that some components contribute more to a state of suffering and addiction than others. This is reflected in the less reported withdrawal symptoms and social media use leading to adverse consequences in real life, whereas the item describing salience was markedly more commonly reported. Such experiences are especially relevant in the context of mental health, where some symptoms emerge as more probable contributors, maintainers and consequences of mental unease. Suffering frequent withdrawal symptoms such as anxiety and irritability might increase existent mental distress. If the individual then finds these moods being alleviated through social media, the component of mood modification emerges as an important element. For individuals without manifest mental disorders, times of stress and tension can lead to escapism for the same reasons. If psychosocial needs are better met online, addiction can manifest as an overreliance on virtual interactions. The difficulty to control use, or stopping it completely, may contribute to general stress and feeling a lack of free will, as well as shame and guilt, two common features of depression. These mind states could in turn be distracted from, but also exaggerated by, using social media. Anxious people may resort to Facebook, Instagram, Snapchat or YouTube if it can provide them with more control over social situations. For example, choosing with greater freedom who to interact with, or dare to express opinions more readily, especially if their anxiety revolves around social situations. Be it depression or anxiety, symptoms might be further exacerbated should social media absorb enough time and energy to damage relationships, work or studies, a potentially serious and debilitating consequence of any addiction. Naturally, the magnitude of these consequences is likely to be smaller than those seen in severe addictions to gambling or substances.

Although there was a significant difference between the groups studied with respect to the amount of time spent communicating in instant messaging services, our results also show that the majority of those who spent more than four hours chatting did not qualify for problematic use. Naturally, this is not equal to them never experiencing distress. However, it indicates that users might well use online chats as their main channel of communication, and in that context, the terminology of ‘addiction’ may present a risk to pathologize and stigmatize common behaviors, especially when they consume a lot of time. Among its many young users, a number of benefits are reported, such as a sense of belonging, establishment of identity, learning skills, as well as improve relationships with virtual and real-world friends. Moreover, attitudes differ between technology when used excessively by children; they themselves regard it as less of a problem or an addiction for that matter, compared to their parents who more often see screen time as negative [ 3 ]. In a debate paper, Kardefeldt and co-workers argue that many behaviors can be performed in excess without being perceived as addictive for the individual. Working eight hours per day is commonplace and can come out of necessity and duty, enjoyment and a sense of purpose, rather than escapism, compulsiveness, or cravings. Professionals of arts or sports, including e-sports, may well compromise other life areas or experience distress when unable to do what they like [ 10 ], although the concept of an addictive condition may be less applicable to these situations.

Also utilizing the BSMAS, in their large study Andreassen and co-workers found that excessive use of social media was linked to symptoms of ADHD and OCD, as well as narcissistic and extraversion personality traits [ 23 , 29 ]. Even though these respondents scored higher on the social media addiction scale, it does not necessarily translate into addiction. The paths to excessive use and addiction-like behavior are thought to be different for people with these traits, with decreased executive functioning or compulsiveness leading to loss of the control that technology has on these individuals, while they might not experience cravings or withdrawal. Such potential associations also should be borne in mind when assessing whether a description of an addiction is appropriate or not.

Strengths and limitations

Most empirical studies into addictive technological behaviors rely on small and/or non-representative samples [ 25 , 71 – 73 ], often surveying students form primary school to university. The sample used in the present study was moderately sized, comprising 2002 respondents including individuals of 16 years and above. However, our sample has a skewed age distribution and is therefore not completely nationally representative, despite an overall ambition to reach representativity. Consequently, any conclusions for prevalence rates of problematic social media cannot be made from our results.

An obvious limitation of the present finding is the fact that all data were self-reported, and although using established scales for the measure of several constructs included here, the self-report of several measures means that the individual’s own perception of one’s behavior may affect the reporting. This could, for example, apply to the reporting of the number of days spent in social messaging services per day; a person with an impression of being more preoccupied than desired with these services, could possibly lead to lower her/his reporting.

Using a market survey company made it possible to disidentify respondents’ IP-addresses, while simultaneously enabling detection of duplicates. Had the same respondent filled the questionnaire more than once from different IPs, the design could not account for that. The length of the questionnaire may, however, reasonably make such an attempt unappealing.

The choice to omit some variables is motivated by initially weak hypotheses, and retrospectively discovered correlational overlap. The reason for including the demographic variables of gross monthly income and living situation is mostly that previous studies carried out by the same research department as this study have included them as well. One could argue that they needed not to be part of the questionnaire only for this reason. Seeking help for mental distress and PRIUSS scoring were found to skew the correlational data of the logistic regression analyses, which might question the choice to include them from the beginning.

One flaw of the addiction scales is that they assess symptomatology for different time frames, rendering them less intercomparable to each other. They range from the past 30 days to the past 12 months, and while PRIUSS is measuring momentaneous experience of Internet use, it is a factor not taken into consideration since it was omitted from regression analysis. The scales are validated and cross-validated across languages, justifying them as tools and comparing them with other studies using them. Naturally, comparing with results stemming from other tools is more speculative. An agreed upon research method would diminish this issue.

It is important to note that all scales are screening tools and not diagnostic instruments, meaning that the demonstrated relationships are based on scores and cutoffs of these scales. Therefore, it is not possible to draw conclusions on if, and to which degree the actual diagnoses co-occur. While this is a limitation of the study with respect to the lack of formal diagnostic information, the use of brief screening tools was based on the need to maintain a relatively brief total survey content, in the format of an online survey.

Study implications

Defined treatment strategies specifically targeting social media addiction are lacking, although extensive work by Young [ 49 ] laid a fundament for cognitive behavioral therapy for general Internet addiction. Since neither the Internet nor social media can reasonably be taken away from people, abstinence protocols such as those used for substances cannot reasonably be applied. Rather, approaches focus on restriction. Managing Internet use in general has traditionally equaled to self-blocking of various websites more prone to addiction, such as pornography or shopping sites. In a social media context, this kind of selective restriction may be hindered by the fact that potentially beneficial and dangerous services exist on the same web page. Limiting access to specific functions on Facebook, Instagram or any other platform might hopefully be enabled by the companies themselves or third parties in the near future. In fact, some appliances have already implemented regulators of use such as app-stop timers, screen time tracking, and proposals to remove the infinite scroll function as well as limiting like-buttons have been made.

Being that problematic use of social is associated with mental distress as well as with problematic gaming and problem gambling, this could motivate health care professionals meeting patients suffering from these associated disorders to also ask them about their social media habits and experiences. One implication is that more research is needed in order to establish possible causality, which cannot be concluded from the present study, but still, there may be implications for routine screening and assessment in clinical settings where any of these conditions are treated, and where other potentially addictive behaviors, or poor mental health, also may be present.

The present study demonstrated a complex interrelationship between problematic use of social media and mental distress, with time spent in instant messaging services, as well as with other behavioral addictions, but not with a history of treatment needs for substance use. Young age was also associated with problematic social media use, while no associations were observed for educational level or occupational status. Conclusions cannot be drawn for the general population as our sample was somewhat skewed in regard to age. Since the measuring tools used are for screening purposes, we cannot conclude whether any corresponding disorders are related either, but rather that higher scores on the scales are seen with one another.

To further expand the emerging field of technology-related addictions, agreed upon definitions and measurement tools, as well as more studies are needed. With longitudinal data, it will be very interesting to see how the use, abuse, or restricted use of social media will contribute to functioning and perception of the individual. Because of their early exposure and norms of technology, observing young generations as they age would be especially fascinating. Given the results of the present study, mental health and social media use will be important to detect and follow in preventive work and in clinical settings.

Supporting information

Funding statement.

The authors received no specific funding for this work.

Data Availability

• Alzheimer's disease & dementia
• Arthritis & Rheumatism
• Attention deficit disorders
• Autism spectrum disorders
• Biomedical technology
• Diseases, Conditions, Syndromes
• Endocrinology & Metabolism
• Gastroenterology
• Gerontology & Geriatrics
• Health informatics
• Inflammatory disorders
• Medical economics
• Medical research
• Medications
• Neuroscience
• Obstetrics & gynaecology
• Oncology & Cancer
• Ophthalmology
• Overweight & Obesity
• Parkinson's & Movement disorders
• Psychology & Psychiatry
• Radiology & Imaging
• Sleep disorders
• Sports medicine & Kinesiology
• Vaccination
• Breast cancer
• Cardiovascular disease
• Chronic obstructive pulmonary disease
• Colon cancer
• Coronary artery disease
• Heart attack
• Heart disease
• High blood pressure
• Kidney disease
• Lung cancer
• Multiple sclerosis
• Myocardial infarction
• Ovarian cancer
• Post traumatic stress disorder
• Rheumatoid arthritis
• Schizophrenia
• Skin cancer
• Type 2 diabetes
• Full List »

share this!

July 4, 2024

This article has been reviewed according to Science X's editorial process and policies . Editors have highlighted the following attributes while ensuring the content's credibility:

fact-checked

trusted source

written by researcher(s)

What can you do if you think your teen already has unhealthy social media habits?

by Carmel Taddeo, The Conversation

Many parents are worried about how much their children use social media and what content they might encounter while using it.

Amid proposals to ban teenagers under 16yrs from social media and calls to better educate them about being safe online, how can you tell if your child's social media use is already a problem? And what steps can take to help if there is an issue?

It is easy to get hooked

These platforms are designed in a way that releases dopamine (the "feel-good" hormone) for users. This can make it especially difficult for adolescents to resist.

As psychologists explain , from the onset of puberty until the mid-20's, our brains are hypersensitive to social feedback and stimuli.

This means young people are more likely to engage in behaviors that can lead to praise or attention from peers and others. So it can be harder for a young person to resist responding to notifications or "likes". Young people also are developing their impulse control, which can have implications for their scrolling habits and make it harder for them to stop.

There are benefits and risks

There are certainly many benefits to social media use , such as social connection, information and support. But there are also risks.

Although it is not necessarily causal, there are links between social media use and depression, anxiety, stress, sleep disorders , many aspects of cyberbullying and body image issues .

So it is understandable if parents have concerns about their children's use of platforms such as TikTok, Instagram or Snapchat.

Are we talking about a 'problem' or an 'addiction'?

Often terms such as "problematic" and "addiction" are used interchangeably when talking about social media use.

But there is no consensus among experts about how to differentiate between them or even if they should be differentiated.

Although some researchers argue social media addiction presents in similar ways to other behavioral addictions such as gambling, it is not recognized as a clinical disorder.

We need to be careful about referring to social media use as a clinical disorder or addiction . It can be more helpful to use terms such as "problematic" or "unhealthy" until we know more.

Is there any advice on how much time is OK?

Australian guidelines suggest children and young people between five and 17 should spend no more than two hours of sedentary recreational screen time per day (not including school work ).

But as the eSafety Commissioner notes , there is no "magic figure". It depends on a range of factors such as a child's maturity and age, the type and quality of content they are consuming and if it involves watching alone or with a caregiver .

There are no specific guidelines around social media use.

How can you tell if there's a problem?

Keeping track of what teenagers are doing online can be very difficult, especially if they have their own devices.

So it means parents and caregivers will have to carefully consider their own child's situation and behavior to work out if there is a issue. Things that can suggest a young person's use of social media has become a problem include:

• withdrawing from, or missing out on usual activities to spend time on social media
• finding it hard to stop or reduce the time spent on social media
• lying about or trying to cover up their social media use
• continuing to use social media even if it is causing problems with their real life relationships or other areas of life (such as school, work or sport).

What about problematic content?

On top of time spent on social media, problematic use can also relate to the kinds of content a young person is being exposed to. This can include content which shows or promotes risky behaviors or violence, extremist views, pornography, gambling opportunities, graphic videos, fake news or mis/disinformation.

This can be very easy to access. As a US Surgeon General's advisory notes , inappropriate content is even directed towards young people through algorithms.

If your child has come across inappropriate or concerning content, they may not want to talk about it or tell anyone because they may be embarrassed, confused or scared.

What can you do if you think there's a problem?

Try to approach a conversation with your child in a sensitive way. Assure them you are here to help and not "get them in trouble".

Thinking about your own social media use can be a useful starting point. Research suggests adolescents are more likely to have problematic internet use in general when their parents also have problematic use. Are your own habits consistent with what you want for your child? Do you have time-out from social media?

You and your child/young person could have a discussion about how you could both commit to changing your behavior as a family. Perhaps this means no social media after a certain time of day or only at certain times of the day.

Involve your kids in change, do things offline

Even if your own habits are OK, it is important for young people to be involved and consulted about what will work for them, rather than an outright "ban" or imposed change. This gives them a sense of ownership of the solution (and makes them more likely to participate).

Research also suggests having regular, positive family time together can help foster time away from devices and problematic use. So organize things that fit with your child's interests and can be done offline. For example, board game nights, hikes, bike rides or meals.

Young people also often seek help and information about problems through other trusted adults and peers . So if you can, encourage them to talk to their friends or a teacher at school about what they do to manage social media use.

Other resources

Problematic social media use is a complex issue. And it needs involvement from the broader community, not just families and caregivers. Any solutions will also need to actively involve young people and social media platforms themselves .

If your child/young person is demonstrating problematic use, and you would like more specific support, contact a counselor or mental health professional.

There are also other resources that may help, including:

• general advice to parents on what social media is and why young people use it, from youth mental health organization ReachOut
• advice to young people if they feel pressured by social media from the eSafety Commissioner
• advice to parents about social media use from the federal government's Student Wellbeing Hub
• advice about the law and social media and what to do if you get into trouble from Youth Law Australia
• advice to young people about how to protect their mental health on social media from Kids Helpline .

Explore further

Feedback to editors

Hepatitis C leaves 'scars' in immune cells even after successful treatment

12 minutes ago

Run screaming or slow retreat? New study sheds light on brain responses to emotionally-charged scenes

4 hours ago

Combining popular diabetes drugs offers complementary heart and kidney benefits, research finds

14 hours ago

Highly accurate AI model can estimate lung function just by using chest X-rays

A research team may have discovered the neurological mechanism underlying a common eating disorder

16 hours ago

Study: Raw milk is risky, but airborne transmission of H5N1 from cow's milk is inefficient in mammals

Erasing 'bad memories' to improve long term Parkinson's disease treatment

Researchers discover the role of RNF114B, a key gene in the progression and treatment of lung adenocarcinoma

Small molecules induce trained immunity, opening a new approach to fighting disease

17 hours ago

Researchers find a common immune system mechanism between pregnancy and cancer

Related stories.

Which adolescents are at risk of depression following early social media use?

Jun 26, 2024

Surgeon general calls on Congress to require social media warning labels, like those on cigarettes

Jun 17, 2024

The people who are most active on social media are also the most active offline, shows study

May 2, 2024

We know social media bans are unlikely to work. So how can we keep young people safe online?

Jun 25, 2024

Study shows more than just social media use may be causing depression in young adults

May 29, 2024

Social media poses considerable risks of psychological harm: How to protect children and adolescents

Recommended for you.

New research supports 'social good' as a cognitive approach to dealing with highly stressful events

Boys born with higher natural resistance to HIV, study finds

23 hours ago

Study identifies racial and gender disparities in youth psychiatric emergency department boarding

21 hours ago

Study links social and non-social synchrony to romantic attractiveness

Jul 6, 2024

Let us know if there is a problem with our content

Use this form if you have come across a typo, inaccuracy or would like to send an edit request for the content on this page. For general inquiries, please use our contact form . For general feedback, use the public comments section below (please adhere to guidelines ).

Please select the most appropriate category to facilitate processing of your request

Thank you for taking time to provide your feedback to the editors.

Your feedback is important to us. However, we do not guarantee individual replies due to the high volume of messages.

E-mail the story

Your email address is used only to let the recipient know who sent the email. Neither your address nor the recipient's address will be used for any other purpose. The information you enter will appear in your e-mail message and is not retained by Medical Xpress in any form.

Newsletter sign up

Get weekly and/or daily updates delivered to your inbox. You can unsubscribe at any time and we'll never share your details to third parties.

More information Privacy policy

Donate and enjoy an ad-free experience

We keep our content available to everyone. Consider supporting Science X's mission by getting a premium account.

E-mail newsletter

Social Media Addiction Among Youth

• University of Creative Technology Chittagong

Discover the world's research

• 25+ million members
• 160+ million publication pages
• 2.3+ billion citations
• Recruit researchers
• Join for free
• Login Email Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google Welcome back! Please log in. Email · Hint Tip: Most researchers use their institutional email address as their ResearchGate login Password Forgot password? Keep me logged in Log in or Continue with Google No account? Sign up

SYSTEMATIC REVIEW article

Research trends in social media addiction and problematic social media use: a bibliometric analysis.

• 1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand
• 2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand

Despite their increasing ubiquity in people's lives and incredible advantages in instantly interacting with others, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays in mental health. Much research has discovered how habitual social media use may lead to addiction and negatively affect adolescents' school performance, social behavior, and interpersonal relationships. The present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. Bibliometric analysis was conducted on 501 articles that were extracted from the Scopus database using the keywords social media addiction and problematic social media use. The data were then uploaded to VOSviewer software to analyze citations, co-citations, and keyword co-occurrences. Volume, growth trajectory, geographic distribution of the literature, influential authors, intellectual structure of the literature, and the most prolific publishing sources were analyzed. The bibliometric analysis presented in this paper shows that the US, the UK, and Turkey accounted for 47% of the publications in this field. Most of the studies used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, the findings in this study show that most analysis were cross-sectional. Studies were performed on undergraduate students between the ages of 19–25 on the use of two social media platforms: Facebook and Instagram. Limitations as well as research directions for future studies are also discussed.

Introduction

Social media generally refers to third-party internet-based platforms that mainly focus on social interactions, community-based inputs, and content sharing among its community of users and only feature content created by their users and not that licensed from third parties ( 1 ). Social networking sites such as Facebook, Instagram, and TikTok are prominent examples of social media that allow people to stay connected in an online world regardless of geographical distance or other obstacles ( 2 , 3 ). Recent evidence suggests that social networking sites have become increasingly popular among adolescents following the strict policies implemented by many countries to counter the COVID-19 pandemic, including social distancing, “lockdowns,” and quarantine measures ( 4 ). In this new context, social media have become an essential part of everyday life, especially for children and adolescents ( 5 ). For them such media are a means of socialization that connect people together. Interestingly, social media are not only used for social communication and entertainment purposes but also for sharing opinions, learning new things, building business networks, and initiate collaborative projects ( 6 ).

Among the 7.91 billion people in the world as of 2022, 4.62 billion active social media users, and the average time individuals spent using the internet was 6 h 58 min per day with an average use of social media platforms of 2 h and 27 min ( 7 ). Despite their increasing ubiquity in people's lives and the incredible advantages they offer to instantly interact with people, an increasing number of studies have linked social media use to negative mental health consequences, such as suicidality, loneliness, and anxiety ( 8 ). Numerous sources have expressed widespread concern about the effects of social media on mental health. A 2011 report by the American Academy of Pediatrics (AAP) identifies a phenomenon known as Facebook depression which may be triggered “when preteens and teens spend a great deal of time on social media sites, such as Facebook, and then begin to exhibit classic symptoms of depression” ( 9 ). Similarly, the UK's Royal Society for Public Health (RSPH) claims that there is a clear evidence of the relationship between social media use and mental health issues based on a survey of nearly 1,500 people between the ages of 14–24 ( 10 ). According to some authors, the increase in usage frequency of social media significantly increases the risks of clinical disorders described (and diagnosed) as “Facebook depression,” “fear of missing out” (FOMO), and “social comparison orientation” (SCO) ( 11 ). Other risks include sexting ( 12 ), social media stalking ( 13 ), cyber-bullying ( 14 ), privacy breaches ( 15 ), and improper use of technology. Therefore, social media's impact on subjective well-being is a source of concern worldwide and calls for up-to-date investigations of the role social media plays with regard to mental health ( 8 ). Many studies have found that habitual social media use may lead to addiction and thus negatively affect adolescents' school performance, social behavior, and interpersonal relationships ( 16 – 18 ). As a result of addiction, the user becomes highly engaged with online activities motivated by an uncontrollable desire to browse through social media pages and “devoting so much time and effort to it that it impairs other important life areas” ( 19 ).

Given these considerations, the present study was conducted to review the extant literature in the domain of social media and analyze global research productivity during 2013–2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” This is valuable as it allows for a comprehensive overview of the current state of this field of research, as well as identifies any patterns or trends that may be present. Additionally, it provides information on the geographical distribution and prolific authors in this area, which may help to inform future research endeavors.

In terms of bibliometric analysis of social media addiction research, few studies have attempted to review the existing literature in the domain extensively. Most previous bibliometric studies on social media addiction and problematic use have focused mainly on one type of screen time activity such as digital gaming or texting ( 20 ) and have been conducted with a focus on a single platform such as Facebook, Instagram, or Snapchat ( 21 , 22 ). The present study adopts a more comprehensive approach by including all social media platforms and all types of screen time activities in its analysis.

Additionally, this review aims to highlight the major themes around which the research has evolved to date and draws some guidance for future research directions. In order to meet these objectives, this work is oriented toward answering the following research questions:

(1) What is the current status of research focusing on social media addiction?

(2) What are the key thematic areas in social media addiction and problematic use research?

(3) What is the intellectual structure of social media addiction as represented in the academic literature?

(4) What are the key findings of social media addiction and problematic social media research?

(5) What possible future research gaps can be identified in the field of social media addiction?

These research questions will be answered using bibliometric analysis of the literature on social media addiction and problematic use. This will allow for an overview of the research that has been conducted in this area, including information on the most influential authors, journals, countries of publication, and subject areas of study. Part 2 of the study will provide an examination of the intellectual structure of the extant literature in social media addiction while Part 3 will discuss the research methodology of the paper. Part 4 will discuss the findings of the study followed by a discussion under Part 5 of the paper. Finally, in Part 7, gaps in current knowledge about this field of research will be identified.

Literature review

Social media addiction research context.

Previous studies on behavioral addictions have looked at a lot of different factors that affect social media addiction focusing on personality traits. Although there is some inconsistency in the literature, numerous studies have focused on three main personality traits that may be associated with social media addiction, namely anxiety, depression, and extraversion ( 23 , 24 ).

It has been found that extraversion scores are strongly associated with increased use of social media and addiction to it ( 25 , 26 ). People with social anxiety as well as people who have psychiatric disorders often find online interactions extremely appealing ( 27 ). The available literature also reveals that the use of social media is positively associated with being female, single, and having attention deficit hyperactivity disorder (ADHD), obsessive compulsive disorder (OCD), or anxiety ( 28 ).

In a study by Seidman ( 29 ), the Big Five personality traits were assessed using Saucier's ( 30 ) Mini-Markers Scale. Results indicated that neurotic individuals use social media as a safe place for expressing their personality and meet belongingness needs. People affected by neurosis tend to use online social media to stay in touch with other people and feel better about their social lives ( 31 ). Narcissism is another factor that has been examined extensively when it comes to social media, and it has been found that people who are narcissistic are more likely to become addicted to social media ( 32 ). In this case users want to be seen and get “likes” from lots of other users. Longstreet and Brooks ( 33 ) did a study on how life satisfaction depends on how much money people make. Life satisfaction was found to be negatively linked to social media addiction, according to the results. When social media addiction decreases, the level of life satisfaction rises. But results show that in lieu of true-life satisfaction people use social media as a substitute (for temporary pleasure vs. longer term happiness).

Researchers have discovered similar patterns in students who tend to rank high in shyness: they find it easier to express themselves online rather than in person ( 34 , 35 ). With the use of social media, shy individuals have the opportunity to foster better quality relationships since many of their anxiety-related concerns (e.g., social avoidance and fear of social devaluation) are significantly reduced ( 36 , 37 ).

Problematic use of social media

The amount of research on problematic use of social media has dramatically increased since the last decade. But using social media in an unhealthy manner may not be considered an addiction or a disorder as this behavior has not yet been formally categorized as such ( 38 ). Although research has shown that people who use social media in a negative way often report negative health-related conditions, most of the data that have led to such results and conclusions comprise self-reported data ( 39 ). The dimensions of excessive social media usage are not exactly known because there are not enough diagnostic criteria and not enough high-quality long-term studies available yet. This is what Zendle and Bowden-Jones ( 40 ) noted in their own research. And this is why terms like “problematic social media use” have been used to describe people who use social media in a negative way. Furthermore, if a lot of time is spent on social media, it can be hard to figure out just when it is being used in a harmful way. For instance, people easily compare their appearance to what they see on social media, and this might lead to low self-esteem if they feel they do not look as good as the people they are following. According to research in this domain, the extent to which an individual engages in photo-related activities (e.g., taking selfies, editing photos, checking other people's photos) on social media is associated with negative body image concerns. Through curated online images of peers, adolescents face challenges to their self-esteem and sense of self-worth and are increasingly isolated from face-to-face interaction.

To address this problem the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) has been used by some scholars ( 41 , 42 ). These scholars have used criteria from the DSM-V to describe one problematic social media use, internet gaming disorder, but such criteria could also be used to describe other types of social media disorders. Franchina et al. ( 43 ) and Scott and Woods ( 44 ), for example, focus their attention on individual-level factors (like fear of missing out) and family-level factors (like childhood abuse) that have been used to explain why people use social media in a harmful way. Friends-level factors have also been explored as a social well-being measurement to explain why people use social media in a malevolent way and demonstrated significant positive correlations with lower levels of friend support ( 45 ). Macro-level factors have also been suggested, such as the normalization of surveillance ( 46 ) and the ability to see what people are doing online ( 47 ). Gender and age seem to be highly associated to the ways people use social media negatively. Particularly among girls, social media use is consistently associated with mental health issues ( 41 , 48 , 49 ), an association more common among older girls than younger girls ( 46 , 48 ).

Most studies have looked at the connection between social media use and its effects (such as social media addiction) and a number of different psychosomatic disorders. In a recent study conducted by Vannucci and Ohannessian ( 50 ), the use of social media appears to have a variety of effects “on psychosocial adjustment during early adolescence, with high social media use being the most problematic.” It has been found that people who use social media in a harmful way are more likely to be depressed, anxious, have low self-esteem, be more socially isolated, have poorer sleep quality, and have more body image dissatisfaction. Furthermore, harmful social media use has been associated with unhealthy lifestyle patterns (for example, not getting enough exercise or having trouble managing daily obligations) as well as life threatening behaviors such as illicit drug use, excessive alcohol consumption and unsafe sexual practices ( 51 , 52 ).

A growing body of research investigating social media use has revealed that the extensive use of social media platforms is correlated with a reduced performance on cognitive tasks and in mental effort ( 53 ). Overall, it appears that individuals who have a problematic relationship with social media or those who use social media more frequently are more likely to develop negative health conditions.

Social media addiction and problematic use systematic reviews

Previous studies have revealed the detrimental impacts of social media addiction on users' health. A systematic review by Khan and Khan ( 20 ) has pointed out that social media addiction has a negative impact on users' mental health. For example, social media addiction can lead to stress levels rise, loneliness, and sadness ( 54 ). Anxiety is another common mental health problem associated with social media addiction. Studies have found that young adolescents who are addicted to social media are more likely to suffer from anxiety than people who are not addicted to social media ( 55 ). In addition, social media addiction can also lead to physical health problems, such as obesity and carpal tunnel syndrome a result of spending too much time on the computer ( 22 ).

Apart from the negative impacts of social media addiction on users' mental and physical health, social media addiction can also lead to other problems. For example, social media addiction can lead to financial problems. A study by Sharif and Yeoh ( 56 ) has found that people who are addicted to social media tend to spend more money than those who are not addicted to social media. In addition, social media addiction can also lead to a decline in academic performance. Students who are addicted to social media are more likely to have lower grades than those who are not addicted to social media ( 57 ).

Research methodology

Bibliometric analysis.

Merigo et al. ( 58 ) use bibliometric analysis to examine, organize, and analyze a large body of literature from a quantitative, objective perspective in order to assess patterns of research and emerging trends in a certain field. A bibliometric methodology is used to identify the current state of the academic literature, advance research. and find objective information ( 59 ). This technique allows the researchers to examine previous scientific work, comprehend advancements in prior knowledge, and identify future study opportunities.

To achieve this objective and identify the research trends in social media addiction and problematic social media use, this study employs two bibliometric methodologies: performance analysis and science mapping. Performance analysis uses a series of bibliometric indicators (e.g., number of annual publications, document type, source type, journal impact factor, languages, subject area, h-index, and countries) and aims at evaluating groups of scientific actors on a particular topic of research. VOSviewer software ( 60 ) was used to carry out the science mapping. The software is used to visualize a particular body of literature and map the bibliographic material using the co-occurrence analysis of author, index keywords, nations, and fields of publication ( 61 , 62 ).

Data collection

After picking keywords, designing the search strings, and building up a database, the authors conducted a bibliometric literature search. Scopus was utilized to gather exploration data since it is a widely used database that contains the most comprehensive view of the world's research output and provides one of the most effective search engines. If the research was to be performed using other database such as Web Of Science or Google Scholar the authors may have obtained larger number of articles however they may not have been all particularly relevant as Scopus is known to have the most widest and most relevant scholar search engine in marketing and social science. A keyword search for “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. The information was gathered in March 2022, and because the Scopus database is updated on a regular basis, the results may change in the future. Next, the authors examined the titles and abstracts to see whether they were relevant to the topics treated. There were two common grounds for document exclusion. First, while several documents emphasized the negative effects of addiction in relation to the internet and digital media, they did not focus on social networking sites specifically. Similarly, addiction and problematic consumption habits were discussed in relation to social media in several studies, although only in broad terms. This left a total of 511 documents. Articles were then limited only to journal articles, conference papers, reviews, books, and only those published in English. This process excluded 10 additional documents. Then, the relevance of the remaining articles was finally checked by reading the titles, abstracts, and keywords. Documents were excluded if social networking sites were only mentioned as a background topic or very generally. This resulted in a final selection of 501 research papers, which were then subjected to bibliometric analysis (see Figure 1 ).

Figure 1 . Preferred reporting items for systematic reviews and meta-analysis (PRISMA) flowchart showing the search procedures used in the review.

After identifying 501 Scopus files, bibliographic data related to these documents were imported into an Excel sheet where the authors' names, their affiliations, document titles, keywords, abstracts, and citation figures were analyzed. These were subsequently uploaded into VOSViewer software version 1.6.8 to begin the bibliometric review. Descriptive statistics were created to define the whole body of knowledge about social media addiction and problematic social media use. VOSViewer was used to analyze citation, co-citation, and keyword co-occurrences. According to Zupic and Cater ( 63 ), co-citation analysis measures the influence of documents, authors, and journals heavily cited and thus considered influential. Co-citation analysis has the objective of building similarities between authors, journals, and documents and is generally defined as the frequency with which two units are cited together within the reference list of a third article.

The implementation of social media addiction performance analysis was conducted according to the models recently introduced by Karjalainen et al. ( 64 ) and Pattnaik ( 65 ). Throughout the manuscript there are operational definitions of relevant terms and indicators following a standardized bibliometric approach. The cumulative academic impact (CAI) of the documents was measured by the number of times they have been cited in other scholarly works while the fine-grained academic impact (FIA) was computed according to the authors citation analysis and authors co-citation analysis within the reference lists of documents that have been specifically focused on social media addiction and problematic social media use.

Results of the study presented here include the findings on social media addiction and social media problematic use. The results are presented by the foci outlined in the study questions.

Volume, growth trajectory, and geographic distribution of the literature

After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use of social media, the authors obtained a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books and 1 conference review. The included literature was very recent. As shown in Figure 2 , publication rates started very slowly in 2013 but really took off in 2018, after which publications dramatically increased each year until a peak was reached in 2021 with 195 publications. Analyzing the literature published during the past decade reveals an exponential increase in scholarly production on social addiction and its problematic use. This might be due to the increasingly widespread introduction of social media sites in everyday life and the ubiquitous diffusion of mobile devices that have fundamentally impacted human behavior. The dip in the number of publications in 2022 is explained by the fact that by the time the review was carried out the year was not finished yet and therefore there are many articles still in press.

Figure 2 . Annual volume of social media addiction or social media problematic use ( n = 501).

The geographical distribution trends of scholarly publications on social media addiction or problematic use of social media are highlighted in Figure 3 . The articles were assigned to a certain country according to the nationality of the university with whom the first author was affiliated with. The figure shows that the most productive countries are the USA (92), the U.K. (79), and Turkey ( 63 ), which combined produced 236 articles, equal to 47% of the entire scholarly production examined in this bibliometric analysis. Turkey has slowly evolved in various ways with the growth of the internet and social media. Anglo-American scholarly publications on problematic social media consumer behavior represent the largest research output. Yet it is interesting to observe that social networking sites studies are attracting many researchers in Asian countries, particularly China. For many Chinese people, social networking sites are a valuable opportunity to involve people in political activism in addition to simply making purchases ( 66 ).

Figure 3 . Global dispersion of social networking sites in relation to social media addiction or social media problematic use.

Analysis of influential authors

This section analyses the high-impact authors in the Scopus-indexed knowledge base on social networking sites in relation to social media addiction or problematic use of social media. It provides valuable insights for establishing patterns of knowledge generation and dissemination of literature about social networking sites relating to addiction and problematic use.

Table 1 acknowledges the top 10 most highly cited authors with the highest total citations in the database.

Table 1 . Highly cited authors on social media addiction and problematic use ( n = 501).

Table 1 shows that MD Griffiths (sixty-five articles), CY Lin (twenty articles), and AH Pakpour (eighteen articles) are the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use . If the criteria are changed and authors ranked according to the overall number of citations received in order to determine high-impact authors, the same three authors turn out to be the most highly cited authors. It should be noted that these highly cited authors tend to enlist several disciplines in examining social media addiction and problematic use. Griffiths, for example, focuses on behavioral addiction stemming from not only digital media usage but also from gambling and video games. Lin, on the other hand, focuses on the negative effects that the internet and digital media can have on users' mental health, and Pakpour approaches the issue from a behavioral medicine perspective.

Intellectual structure of the literature

In this part of the paper, the authors illustrate the “intellectual structure” of the social media addiction and the problematic use of social media's literature. An author co-citation analysis (ACA) was performed which is displayed as a figure that depicts the relations between highly co-cited authors. The study of co-citation assumes that strongly co-cited authors carry some form of intellectual similarity ( 67 ). Figure 4 shows the author co-citation map. Nodes represent units of analysis (in this case scholars) and network ties represent similarity connections. Nodes are sized according to the number of co-citations received—the bigger the node, the more co-citations it has. Adjacent nodes are considered intellectually similar.

Figure 4 . Two clusters, representing the intellectual structure of the social media and its problematic use literature.

Scholars belonging to the green cluster (Mental Health and Digital Media Addiction) have extensively published on medical analysis tools and how these can be used to heal users suffering from addiction to digital media, which can range from gambling, to internet, to videogame addictions. Scholars in this school of thought focus on the negative effects on users' mental health, such as depression, anxiety, and personality disturbances. Such studies focus also on the role of screen use in the development of mental health problems and the increasing use of medical treatments to address addiction to digital media. They argue that addiction to digital media should be considered a mental health disorder and treatment options should be made available to users.

In contrast, scholars within the red cluster (Social Media Effects on Well Being and Cyberpsychology) have focused their attention on the effects of social media toward users' well-being and how social media change users' behavior, focusing particular attention on the human-machine interaction and how methods and models can help protect users' well-being. Two hundred and two authors belong to this group, the top co-cited being Andreassen (667 co-citations), Pallasen (555 co-citations), and Valkenburg (215 co-citations). These authors have extensively studied the development of addiction to social media, problem gambling, and internet addiction. They have also focused on the measurement of addiction to social media, cyberbullying, and the dark side of social media.

Most influential source title in the field of social media addiction and its problematic use

To find the preferred periodicals in the field of social media addiction and its problematic use, the authors have selected 501 articles published in 263 journals. Table 2 gives a ranked list of the top 10 journals that constitute the core publishing sources in the field of social media addiction research. In doing so, the authors analyzed the journal's impact factor, Scopus Cite Score, h-index, quartile ranking, and number of publications per year.

Table 2 . Top 10 most cited and more frequently mentioned documents in the field of social media addiction.

The journal Addictive Behaviors topped the list, with 700 citations and 22 publications (4.3%), followed by Computers in Human Behaviors , with 577 citations and 13 publications (2.5%), Journal of Behavioral Addictions , with 562 citations and 17 publications (3.3%), and International Journal of Mental Health and Addiction , with 502 citations and 26 publications (5.1%). Five of the 10 most productive journals in the field of social media addiction research are published by Elsevier (all Q1 rankings) while Springer and Frontiers Media published one journal each.

Documents citation analysis identified the most influential and most frequently mentioned documents in a certain scientific field. Andreassen has received the most citations among the 10 most significant papers on social media addiction, with 405 ( Table 2 ). The main objective of this type of studies was to identify the associations and the roles of different variables as predictors of social media addiction (e.g., ( 19 , 68 , 69 )). According to general addiction models, the excessive and problematic use of digital technologies is described as “being overly concerned about social media, driven by an uncontrollable motivation to log on to or use social media, and devoting so much time and effort to social media that it impairs other important life areas” ( 27 , 70 ). Furthermore, the purpose of several highly cited studies ( 31 , 71 ) was to analyse the connections between young adults' sleep quality and psychological discomfort, depression, self-esteem, and life satisfaction and the severity of internet and problematic social media use, since the health of younger generations and teenagers is of great interest this may help explain the popularity of such papers. Despite being the most recent publication Lin et al.'s work garnered more citations annually. The desire to quantify social media addiction in individuals can also help explain the popularity of studies which try to develop measurement scales ( 42 , 72 ). Some of the highest-ranked publications are devoted to either the presentation of case studies or testing relationships among psychological constructs ( 73 ).

Keyword co-occurrence analysis

The research question, “What are the key thematic areas in social media addiction literature?” was answered using keyword co-occurrence analysis. Keyword co-occurrence analysis is conducted to identify research themes and discover keywords. It mainly examines the relationships between co-occurrence keywords in a wide variety of literature ( 74 ). In this approach, the idea is to explore the frequency of specific keywords being mentioned together.

Utilizing VOSviewer, the authors conducted a keyword co-occurrence analysis to characterize and review the developing trends in the field of social media addiction. The top 10 most frequent keywords are presented in Table 3 . The results indicate that “social media addiction” is the most frequent keyword (178 occurrences), followed by “problematic social media use” (74 occurrences), “internet addiction” (51 occurrences), and “depression” (46 occurrences). As shown in the co-occurrence network ( Figure 5 ), the keywords can be grouped into two major clusters. “Problematic social media use” can be identified as the core theme of the green cluster. In the red cluster, keywords mainly identify a specific aspect of problematic social media use: social media addiction.

Table 3 . Frequency of occurrence of top 10 keywords.

Figure 5 . Keywords co-occurrence map. Threshold: 5 co-occurrences.

The results of the keyword co-occurrence analysis for journal articles provide valuable perspectives and tools for understanding concepts discussed in past studies of social media usage ( 75 ). More precisely, it can be noted that there has been a large body of research on social media addiction together with other types of technological addictions, such as compulsive web surfing, internet gaming disorder, video game addiction and compulsive online shopping ( 76 – 78 ). This field of research has mainly been directed toward teenagers, middle school students, and college students and university students in order to understand the relationship between social media addiction and mental health issues such as depression, disruptions in self-perceptions, impairment of social and emotional activity, anxiety, neuroticism, and stress ( 79 – 81 ).

The findings presented in this paper show that there has been an exponential increase in scholarly publications—from two publications in 2013 to 195 publications in 2021. There were 45 publications in 2022 at the time this study was conducted. It was interesting to observe that the US, the UK, and Turkey accounted for 47% of the publications in this field even though none of these countries are in the top 15 countries in terms of active social media penetration ( 82 ) although the US has the third highest number of social media users ( 83 ). Even though China and India have the highest number of social media users ( 83 ), first and second respectively, they rank fifth and tenth in terms of publications on social media addiction or problematic use of social media. In fact, the US has almost double the number of publications in this field compared to China and almost five times compared to India. Even though East Asia, Southeast Asia, and South Asia make up the top three regions in terms of worldwide social media users ( 84 ), except for China and India there have been only a limited number of publications on social media addiction or problematic use. An explanation for that could be that there is still a lack of awareness on the negative consequences of the use of social media and the impact it has on the mental well-being of users. More research in these regions should perhaps be conducted in order to understand the problematic use and addiction of social media so preventive measures can be undertaken.

From the bibliometric analysis, it was found that most of the studies examined used quantitative methods in analyzing data and therefore aimed at testing relationships between variables. In addition, many studies were empirical, aimed at testing relationships based on direct or indirect observations of social media use. Very few studies used theories and for the most part if they did they used the technology acceptance model and social comparison theories. The findings presented in this paper show that none of the studies attempted to create or test new theories in this field, perhaps due to the lack of maturity of the literature. Moreover, neither have very many qualitative studies been conducted in this field. More qualitative research in this field should perhaps be conducted as it could explore the motivations and rationales from which certain users' behavior may arise.

The authors found that almost all the publications on social media addiction or problematic use relied on samples of undergraduate students between the ages of 19–25. The average daily time spent by users worldwide on social media applications was highest for users between the ages of 40–44, at 59.85 min per day, followed by those between the ages of 35–39, at 59.28 min per day, and those between the ages of 45–49, at 59.23 per day ( 85 ). Therefore, more studies should be conducted exploring different age groups, as users between the ages of 19–25 do not represent the entire population of social media users. Conducting studies on different age groups may yield interesting and valuable insights to the field of social media addiction. For example, it would be interesting to measure the impacts of social media use among older users aged 50 years or older who spend almost the same amount of time on social media as other groups of users (56.43 min per day) ( 85 ).

A majority of the studies tested social media addiction or problematic use based on only two social media platforms: Facebook and Instagram. Although Facebook and Instagram are ranked first and fourth in terms of most popular social networks by number of monthly users, it would be interesting to study other platforms such as YouTube, which is ranked second, and WhatsApp, which is ranked third ( 86 ). Furthermore, TikTok would also be an interesting platform to study as it has grown in popularity in recent years, evident from it being the most downloaded application in 2021, with 656 million downloads ( 87 ), and is ranked second in Q1 of 2022 ( 88 ). Moreover, most of the studies focused only on one social media platform. Comparing different social media platforms would yield interesting results because each platform is different in terms of features, algorithms, as well as recommendation engines. The purpose as well as the user behavior for using each platform is also different, therefore why users are addicted to these platforms could provide a meaningful insight into social media addiction and problematic social media use.

Lastly, most studies were cross-sectional, and not longitudinal, aiming at describing results over a certain point in time and not over a long period of time. A longitudinal study could better describe the long-term effects of social media use.

This study was conducted to review the extant literature in the field of social media and analyze the global research productivity during the period ranging from 2013 to 2022. The study presents a bibliometric overview of the leading trends with particular regard to “social media addiction” and “problematic social media use.” The authors applied science mapping to lay out a knowledge base on social media addiction and its problematic use. This represents the first large-scale analysis in this area of study.

A keyword search of “social media addiction” OR “problematic social media use” yielded 553 papers, which were downloaded from Scopus. After performing the Scopus-based investigation of the current literature regarding social media addiction and problematic use, the authors ended up with a knowledge base consisting of 501 documents comprising 455 journal articles, 27 conference papers, 15 articles reviews, 3 books, and 1 conference review.

The geographical distribution trends of scholarly publications on social media addiction or problematic use indicate that the most productive countries were the USA (92), the U.K. (79), and Turkey ( 63 ), which together produced 236 articles. Griffiths (sixty-five articles), Lin (twenty articles), and Pakpour (eighteen articles) were the most productive scholars according to the number of Scopus documents examined in the area of social media addiction and its problematic use. An author co-citation analysis (ACA) was conducted which generated a layout of social media effects on well-being and cyber psychology as well as mental health and digital media addiction in the form of two research literature clusters representing the intellectual structure of social media and its problematic use.

The preferred periodicals in the field of social media addiction and its problematic use were Addictive Behaviors , with 700 citations and 22 publications, followed by Computers in Human Behavior , with 577 citations and 13 publications, and Journal of Behavioral Addictions , with 562 citations and 17 publications. Keyword co-occurrence analysis was used to investigate the key thematic areas in the social media literature, as represented by the top three keyword phrases in terms of their frequency of occurrence, namely, “social media addiction,” “problematic social media use,” and “social media addiction.”

This research has a few limitations. The authors used science mapping to improve the comprehension of the literature base in this review. First and foremost, the authors want to emphasize that science mapping should not be utilized in place of established review procedures, but rather as a supplement. As a result, this review can be considered the initial stage, followed by substantive research syntheses that examine findings from recent research. Another constraint stems from how 'social media addiction' is defined. The authors overcame this limitation by inserting the phrase “social media addiction” OR “problematic social media use” in the search string. The exclusive focus on SCOPUS-indexed papers creates a third constraint. The SCOPUS database has a larger number of papers than does Web of Science although it does not contain all the publications in a given field.

Although the total body of literature on social media addiction is larger than what is covered in this review, the use of co-citation analyses helped to mitigate this limitation. This form of bibliometric study looks at all the publications listed in the reference list of the extracted SCOPUS database documents. As a result, a far larger dataset than the one extracted from SCOPUS initially has been analyzed.

The interpretation of co-citation maps should be mentioned as a last constraint. The reason is that the procedure is not always clear, so scholars must have a thorough comprehension of the knowledge base in order to make sense of the result of the analysis ( 63 ). This issue was addressed by the authors' expertise, but it remains somewhat subjective.

Implications

The findings of this study have implications mainly for government entities and parents. The need for regulation of social media addiction is evident when considering the various risks associated with habitual social media use. Social media addiction may lead to negative consequences for adolescents' school performance, social behavior, and interpersonal relationships. In addition, social media addiction may also lead to other risks such as sexting, social media stalking, cyber-bullying, privacy breaches, and improper use of technology. Given the seriousness of these risks, it is important to have regulations in place to protect adolescents from the harms of social media addiction.

Regulation of social media platforms

One way that regulation could help protect adolescents from the harms of social media addiction is by limiting their access to certain websites or platforms. For example, governments could restrict adolescents' access to certain websites or platforms during specific hours of the day. This would help ensure that they are not spending too much time on social media and are instead focusing on their schoolwork or other important activities.

Another way that regulation could help protect adolescents from the harms of social media addiction is by requiring companies to put warning labels on their websites or apps. These labels would warn adolescents about the potential risks associated with excessive use of social media.

Finally, regulation could also require companies to provide information about how much time each day is recommended for using their website or app. This would help adolescents make informed decisions about how much time they want to spend on social media each day. These proposed regulations would help to protect children from the dangers of social media, while also ensuring that social media companies are more transparent and accountable to their users.

Parental involvement in adolescents' social media use

Parents should be involved in their children's social media use to ensure that they are using these platforms safely and responsibly. Parents can monitor their children's online activity, set time limits for social media use, and talk to their children about the risks associated with social media addiction.

Education on responsible social media use

Adolescents need to be educated about responsible social media use so that they can enjoy the benefits of these platforms while avoiding the risks associated with addiction. Education on responsible social media use could include topics such as cyber-bullying, sexting, and privacy breaches.

Research directions for future studies

A content analysis was conducted to answer the fifth research questions “What are the potential research directions for addressing social media addiction in the future?” The study reveals that there is a lack of screening instruments and diagnostic criteria to assess social media addiction. Validated DSM-V-based instruments could shed light on the factors behind social media use disorder. Diagnostic research may be useful in order to understand social media behavioral addiction and gain deeper insights into the factors responsible for psychological stress and psychiatric disorders. In addition to cross-sectional studies, researchers should also conduct longitudinal studies and experiments to assess changes in users' behavior over time ( 20 ).

Another important area to examine is the role of engagement-based ranking and recommendation algorithms in online habit formation. More research is required to ascertain how algorithms determine which content type generates higher user engagement. A clear understanding of the way social media platforms gather content from users and amplify their preferences would lead to the development of a standardized conceptualization of social media usage patterns ( 89 ). This may provide a clearer picture of the factors that lead to problematic social media use and addiction. It has been noted that “misinformation, toxicity, and violent content are inordinately prevalent” in material reshared by users and promoted by social media algorithms ( 90 ).

Additionally, an understanding of engagement-based ranking models and recommendation algorithms is essential in order to implement appropriate public policy measures. To address the specific behavioral concerns created by social media, legislatures must craft appropriate statutes. Thus, future qualitative research to assess engagement based ranking frameworks is extremely necessary in order to provide a broader perspective on social media use and tackle key regulatory gaps. Particular emphasis must be placed on consumer awareness, algorithm bias, privacy issues, ethical platform design, and extraction and monetization of personal data ( 91 ).

From a geographical perspective, the authors have identified some main gaps in the existing knowledge base that uncover the need for further research in certain regions of the world. Accordingly, the authors suggest encouraging more studies on internet and social media addiction in underrepresented regions with high social media penetration rates such as Southeast Asia and South America. In order to draw more contributions from these countries, journals with high impact factors could also make specific calls. This would contribute to educating social media users about platform usage and implement policy changes that support the development of healthy social media practices.

The authors hope that the findings gathered here will serve to fuel interest in this topic and encourage other scholars to investigate social media addiction in other contexts on newer platforms and among wide ranges of sample populations. In light of the rising numbers of people experiencing mental health problems (e.g., depression, anxiety, food disorders, and substance addiction) in recent years, it is likely that the number of papers related to social media addiction and the range of countries covered will rise even further.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

AP took care of bibliometric analysis and drafting the paper. VB took care of proofreading and adding value to the paper. AS took care of the interpretation of the findings. All authors contributed to the article and approved the submitted version.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

1. Asur S and Huberman BA. “Predicting the future with social media,” in 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, Vol. 1 . IEEE (2010). p. 492–9. doi: 10.1109/WI-IAT.2010.63

CrossRef Full Text | Google Scholar

2. Kaye LK. Exploring the “socialness” of social media. Comput. Human Behav. Rep. (2021) 3:2021. doi: 10.1016/j.chbr.2021.100083

3. Boyd DM and Ellison NB. Social network sites: definition, history, and scholarship. J Computer-Med Commun. (2007) 13:210–30. doi: 10.1111/j.1083-6101.2007.00393.x

4. Marengo D, Fabris MA, Longobardi C, Settanni M. Smartphone and social media use contributed to individual tendencies towards social media addiction in Italian adolescents during the COVID-19 pandemic. Addictive Behav. (2022) 126:107204. doi: 10.1016/j.addbeh.2021.107204

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Alshamrani S, Abusnaina A, Abuhamad M, Nyang D, Mohaisen D. “Hate, obscenity, and insults: Measuring the exposure of children to inappropriate comments in youtube,” in Companion Proceedings of the Web Conference . (2021). p. 508–515. doi: 10.1145/3442442.3452314

6. Malesev S, Cherry M. Digital and social media marketing-growing market share for construction SMEs. Construction Econom. Build. (2021) 21:65–82. doi: 10.5130/AJCEB.v21i1.7521

7. We Are Social. Digital 2022: Global Overview Report. (2022). Available online at: https://datareportal.com/reports/digital-2022-global-overview-report (accessed July 18, 2022).

Google Scholar

8. Latikka R, Koivula A, Oksa R, Savela N, Oksanen A. Loneliness and psychological distress before and during the COVID-19 pandemic: relationships with social media identity bubbles. Soc Sci Med. (2022) 293:114674. doi: 10.1016/j.socscimed.2021.114674

9. American Academy of Pediatrics. Supporting the health care transition from adolescence to adulthood in the medical home. Pediatrics. (2011) 128:182–200. doi: 10.1542/peds.2018-2587

10. Royal Society for Public Health. Social media addiction: The role of content. J Behav Addict. (2017) 9:64–77. doi: 10.1556/2006.6.2017.058

11. Blease CR. Too many “friends”, too few “likes”? Evolutionary psychology and Facebook depression. Rev. General Psychol. (2015) 19:1–13. doi: 10.1037/gpr0000030

12. Hasinoff AA. Sexting as media production: rethinking social media and sexuality. New Media Soc. (2013) 15:449–65. doi: 10.1177/1461444812459171

13. Sapone L. Moving fast and breaking things: an analysis of social media's revolutionary effects on culture and its impending regulation. Duq L Rev. (2021) 59:362.

14. Whittaker E, Kowalski RM. Cyberbullying via social media. J School Violence. (2015) 14:11–29. doi: 10.1080/15388220.2014.949377

15. Saravanakumar K, Deepa K. On privacy and security in social media–a comprehensive study. Procedia Computer Sci. (2016) 78:114–9. doi: 10.1016/j.procs.2016.02.019

16. Kalika M. Social networking continuance: When habit leads to information overload. Inf Process Manag. (2015) 5:10–31. doi: 10.18151/7217377

17. Kelly Y, Zilanawala A, Booker C, Sacker A. Social media use and adolescent mental health: findings from the UK Millennium cohort study. EClinicalMedicine. (2018) 6:59–68. doi: 10.1016/j.eclinm.2018.12.005

18. Twenge JM, Campbell WK. Associations between screen time and lower psychological well-being among children and adolescents: evidence from a population-based study. Prevent Med Rep. (2018) 12:271–83. doi: 10.1016/j.pmedr.2018.10.003

19. Andreassen CS, Pallesen S, Griffiths MD. The relationship between addictive use of social media, narcissism, and self-esteem: Findings from a large national survey. Addict Behav. (2014) 64:287–93. doi: 10.1016/j.addbeh.2016.03.006

20. Khan NF, Khan MN. A bibliometric analysis of peer-reviewed literature on smartphone addiction and future research agenda. Asia-Pacific J Bus Admin. (2021) 14:199–201. doi: 10.1108/APJBA-09-2021-0430

21. Baran KS, Ghaffari H. The manifold research fields of Facebook: a bibliometric analysis. J Information Sci Theory Pract. (2017) 5:33–47. doi: 10.1633/JISTaP.2017.5.2.3

22. Rejeb A, Rejeb K, Abdollahi A, Treiblmaier H. The big picture on Instagram research: Insights from a bibliometric analysis. Telematics Informat. (2022) 25:101876. doi: 10.1016/j.tele.2022.101876

23. D'Arienzo MC, Boursier V, Griffiths MD. Addiction to social media and attachment styles: A systematic literature review. Int J Ment Health Addict. (2019) 17:1094–118. doi: 10.1007/s11469-019-00082-5

24. Kırcaburun K, Griffiths MD. Problematic Instagram use: The role of perceived feeling of presence and escapism. Int J Ment Health Addict. (2019) 17:909–21. doi: 10.1007/s11469-018-9895-7

25. Ho SS, Lwin MO, Lee EW. Till logout do us part? Comparison of factors predicting excessive social network sites use and addiction between Singaporean adolescents and adults. Comput. Human Behav. (2017) 75:632–42. doi: 10.1016/j.chb.2017.06.002

26. Kuss DJ, Griffiths MD. Online social networking and addiction—a review of the psychological literature. Int J Environ Res Public Health. (2011) 8:3528–52. doi: 10.3390/ijerph8093528

27. Buote VM, Wood E, Pratt M. Exploring similarities and differences between online and offline friendships: The role of attachment style. Comput Human Behav. (2009) 25:560–7. doi: 10.1016/j.chb.2008.12.022

28. Andreassen CS, Billieux J, Griffiths MD, Kuss DJ, Demetrovics Z, Mazzoni E, et al. The relationship between addictive use of social media and video games and symptoms of psychiatric disorders: A large-scale cross-sectional study. Psychol Addict Behav. (2016) 30:252. doi: 10.1037/adb0000160

29. Seidman G. Self-presentation and belonging on Facebook: How personality influences social media use and motivations. Pers Individ Dif. (2013) 54:402–7. doi: 10.1016/j.paid.2012.10.009

30. Saucier G. Mini-Markers: A brief version of Goldberg's unipolar Big-Five markers. J Personal Assess. (1994) 63:506–16. doi: 10.1207/s15327752jpa6303_8

31. Blackwell D, Leaman C, Tramposch R, Osborne C, Liss M. Extraversion, neuroticism, attachment style and fear of missing out as predictors of social media use and addiction. Personal Individ Diff. (2017) 116:69–72. doi: 10.1016/j.paid.2017.04.039

32. Hawk ST, van den Eijnden RJ, van Lissa CJ, ter Bogt TF. Narcissistic adolescents' attention-seeking following social rejection: Links with social media disclosure, problematic social media use, and smartphone stress. Comput Human Behav. (2019) 92:65–75. doi: 10.1016/j.chb.2018.10.032

33. Longstreet P, Brooks S. Life satisfaction: a key to managing internet and social media addiction. Technol Soc. (2017) 50:73–7. doi: 10.1016/j.techsoc.2017.05.003

34. Marriott TC, Buchanan T. The true self online: personality correlates of preference for self-expression online, and observer ratings of personality online and offline. Comput Human Behav. (2014) 32:171–7. doi: 10.1016/j.chb.2013.11.014

35. Orr ES, Sisic M, Ross C, Simmering MG, Arseneault JM, Orr RR. The influence of shyness on the use of Facebook in an undergraduate sample. Cyberpsychol Behav. (2009) 12:337–40. doi: 10.1089/cpb.2008.0214

36. Brunet PM, Schmidt LA. Is shyness context specific? Relation between shyness and online self-disclosure with and without a live webcam in young adults. J Res Pers. (2007) 41:938–45.

37. Roberts SG, Wilson R, Fedurek P, Dunbar RI. Individual differences and personal social network size and structure. Pers Individ Dif. (2000) 44:954–64. doi: 10.1016/j.jrp.2006.09.001

38. Wegmann E, Müller SM, Turel O, Brand M. Interactions of impulsivity, general executive functions, and specific inhibitory control explain symptoms of social-networks-use disorder: an experimental study. Sci Rep. (2020) 10:3866. doi: 10.1038/s41598-020-60819-4

39. Motoki K, Suzuki S, Kawashima R, Sugiura M. A combination of self-reported data and social-related neural measures forecasts viral marketing success on social media. J Interactive Market. (2020) 52:99–117. doi: 10.1016/j.intmar.2020.06.003

40. Zendle D, Bowden-Jones H. Is excessive use of social media an addiction? BMJ . (2019) 365:2171. doi: 10.1136/bmj.l2171

41. Bányai F, Zsila Á, Király O, Maraz A, Elekes Z, Griffiths MD, et al. Problematic social media use: results from a large-scale nationally representative adolescent sample. PLoS ONE. (2017) 12:e0169839. doi: 10.1371/journal.pone.0169839

42. Van den Eijnden RJ, Lemmens JS, Valkenburg PM. The social media disorder scale. Comput Human Behav. (2016) 61:478–87. doi: 10.1016/j.chb.2016.03.038

43. Franchina V, Vanden Abeele M, Van Rooij AJ, Lo Coco G, De Marez L. Fear of missing out as a predictor of problematic social media use and phubbing behavior among Flemish adolescents. Int J Environ Res Public Health. (2018) 15:2319. doi: 10.3390/ijerph15102319

44. Scott H, Woods HC. Fear of missing out and sleep: Cognitive behavioural factors in adolescents' nighttime social media use. J Adolescence. (2018) 68:61–5. doi: 10.1016/j.adolescence.2018.07.009

45. Boer M, Van Den Eijnden RJ, Boniel-Nissim M, Wong SL, Inchley JC, Badura P, et al. Adolescents' intense and problematic social media use and their well-being in 29 countries. J Adolescent Health. (2020) 66:S89–99. doi: 10.1016/j.jadohealth.2020.02.014

46. Paakkari L, Tynjälä J, Lahti H, Ojala K, Lyyra N. Problematic social media use and health among adolescents. Int J Environ Res Public Health. (2021) 18:1885. doi: 10.3390/ijerph18041885

47. Lee EW, Ho SS, Lwin MO. Explicating problematic social network sites use: a review of concepts, theoretical frameworks, and future directions for communication theorizing. N Media Soc. (2017) 19:308–26. doi: 10.1177/1461444816671891

48. Inchley JC, Stevens GW, Samdal O, Currie DB. Enhancing understanding of adolescent health and well-being: the health behaviour in school-aged children study. J Adolescent Health. (2020) 66:S3–5. doi: 10.1016/j.jadohealth.2020.03.014

49. Buda G, Lukoševičiut, e J, Šalčiunaite L, Šmigelskas K. Possible effects of social media use on adolescent health behaviors and perceptions. Psychol Rep. (2021) 124:1031–48. doi: 10.1177/0033294120922481

50. Vannucci A, McCauley Ohannessian C. Social media use subgroups differentially predict psychosocial well-being during early adolescence. J Youth Adolescence. (2019) 48:1469–93. doi: 10.1007/s10964-019-01060-9

51. Lewycka S, Clark T, Peiris-John R, Fenaughty J, Bullen P, Denny S, et al. Downwards trends in adolescent risk-taking behaviours in New Zealand: Exploring driving forces for change. J Paediatr Child Health. (2018) 54:602–8. doi: 10.1111/jpc.13930

52. Vannucci A, Simpson EG, Gagnon S, Ohannessian CM. Social media use and risky behaviors in adolescents: a meta-analysis. J Adolescence. (2020) 79:258–74. doi: 10.1016/j.adolescence.2020.01.014

53. Uncapher MR, Lin L, Rosen LD, Kirkorian HL, Baron NS, Bailey K, et al. Media multitasking and cognitive, psychological, neural, learning differences. Pediatrics . (2017) 140(Suppl 2):S62–6. doi: 10.1542/peds.2016-1758D

54. Ali I, Balta M, Papadopoulos T. Social media platforms and social enterprise: bibliometric analysis and systematic review. Int J Inform Manage. (2022) 10:510. doi: 10.1016/j.ijinfomgt.2022.102510

55. Aparicio-Martinez P, Perea-Moreno AJ, Martinez-Jimenez MP, Redel-Macías MD, Vaquero-Abellan M, Pagliari C. A bibliometric analysis of the health field regarding social networks and young people. Int J Environ Res Public Health. (2019) 16:4024. doi: 10.3390/ijerph16204024

56. Sharif SP, Yeoh KK. Excessive Social Networking Sites Use and Online Compulsive Buying in Young Adults: the Mediating Role of Money Attitude. Young Consumers (2018).

57. Moreno-Guerrero AJ, Gómez-García G, López-Belmonte J, Rodríguez-Jiménez C. Internet addiction in the web of science database: a review of the literature with scientific mapping. Int J Environ Res Public Health. (2020) 17:27–53. doi: 10.3390/ijerph17082753

58. Merig, ó JM, Mas-Tur A, Roig-Tierno N, Ribeiro-Soriano D. A bibliometric overview of the Journal of Business Research between 1973 and 2014. J Business Res. (2015) 68:2645–53. doi: 10.1016/j.jbusres.2015.04.006

59. Kumar S, Pandey N, Burton B, Sureka R. Research patterns and intellectual structure of managerial auditing journal: a retrospective using bibliometric analysis during 1986–2019. Managerial Auditing J . (2021) 36:280–313. doi: 10.1108/MAJ-12-2019-2517

60. Van Eck NJ, Waltman L. CitNetExplorer: a new software tool for analyzing and visualizing citation networks. J Informetr. (2014) 8:802–23. doi: 10.1016/j.joi.2014.07.006

61. Durieux V, Gevenois PA. Bibliometric indicators: quality measurements of scientific publication. Radiology. (2010) 255:342–51. doi: 10.1148/radiol.09090626

62. Small H. Visualizing science by citation mapping. J Am Soc Information Sci. (1999) 50:799–813. doi: 10.1002/(SICI)1097-4571(1999)50:9 < 799::AID-ASI9>3.0.CO;2-G

63. Zupic I, Cater T. Bibliometric methods in management and organization. Organiz Res Methods. (2015) 18:429–72. doi: 10.1177/1094428114562629

64. Karjalainen J, Valtakoski A, KauraNen I. (2019). Interfirm network structure and firm resources: Towards a unifying concept. In: A Network Approach in Strategic Management: Emerging Trends and Research Concepts , Vol. 17. Fundacja Cognitione (2019). p. 227. doi: 10.7341/20211737

65. Pattnaik D. A bibliometric retrospection of marketing from the lens of psychology: Insights from psychology & marketing. Psychol Market. (2020) 38:834–65. doi: 10.1002/mar.21472

66. Chu SC, Chen HT, Gan C. Consumers' engagement with corporate social responsibility (CSR) communication in social media: evidence from China and the United States. J Business Res. (2020) 110:260–71. doi: 10.1016/j.jbusres.2020.01.036

67. Bush VD, Gilbert FW. The web as a medium: an exploratory comparison of internet users versus newspaper readers. J Market Theory Pract. (2002) 10:1–10. doi: 10.1080/10696679.2002.11501905

68. Griffiths MD, Kuss DJ, Demetrovics Z. Social networking addiction: An overview of preliminary findings. Behav Addict. (2014) 2014:119–41. doi: 10.1016/B978-0-12-407724-9.00006-9

69. Tang J, Chang Y, Aggarwal C, Liu H. A survey of signed network mining in social media. ACM Comput Surveys (CSUR). (2016) 49:1–37. doi: 10.1145/2956185

70. Griffiths MD. En “komponenter” modell for avhengighet innenfor en biopsykososial ramme. J Subst-bruk. (2005) 10:191–7.

71. Lin CY, Broström A, Griffiths MD, Pakpour AH. Investigating mediated effects of fear of COVID-19 and COVID-19 misunderstanding in the association between problematic social media use, psychological distress, and insomnia. Internet Intervent. (2020) 21:100345. doi: 10.1016/j.invent.2020.100345

72. Monacis L, De Palo V, Griffiths MD, Sinatra M. Social networking addiction, attachment style, and validation of the Italian version of the Bergen Social Media Addiction Scale. J Behav Addict. (2017) 6:178–86. doi: 10.1556/2006.6.2017.023

73. Gupta K. What is social media? How is it affecting adolescent's mental health? Eur Biomedical J. (2015) 410:51–65. doi: 10.1000/182

74. Zhao Z, Zhu H, Xue Z, Liu Z, Tian J, Chua MCH, et al. An image-text consistency driven multimodal sentiment analysis approach for social media. Inf Process Manag. (2019) 56:102097. doi: 10.1016/j.ipm.2019.102097

75. Small H. Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Informat Sci. (1973) 24:265–9. doi: 10.1002/asi.4630240406

76. Su W, Han X, Yu H, Wu Y, Potenza MN. Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction. Comput Human Behav. (2020) 113:106480. doi: 10.1016/j.chb.2020.106480

77. Albort-Morant G, Henseler J, Leal-Millán A, Cepeda-Carrión G. Mapping the field: a bibliometric analysis of green innovation. Sustainability . (2017) 9:1011. doi: 10.3390/su9061011

78. Wilson K, Fornasier S, White KM. Psychological predictors of young adults' use of social networking sites. Cyberpsychol Behav Social Netw . (2010) 13:173–7. doi: 10.1089/cyber.2009.0094

79. Barbera LD, Paglia LF, Valsavoia R. Social network and addiction. Stud Health Technol Inform. (2009) 144:33–6. doi: 10.3390/ijerph14030311

80. Hussain Z, Griffiths MD. Problematic social networking site use and comorbid psychiatric disorders: a systematic review of recent large-scale studies. Front Psychiatr . (2018) 5:686. doi: 10.3389/fpsyt.2018.00686

81. American Academy of Pediatric Dentistry. Guideline on Psychological Therapy . Chicago: American Academy of Pediatric (2008). Available online at: http://www.aapd.org/media/Policies_Guidelines/PsychologicalTherapy.pdf (accessed July 18, 2022).

82. Statista. Number of Smartphone Subscriptions Worldwide from 2016 to 2026. (2022). Available online at: https://www.statista.com/statistics/330695/number-of-smartphone-users-worldwide/ (accessed July 10, 2022).

83. Statista.com. Share of Global Mobile Website Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/277125/share-of-website-traffic-coming-from-mobile-devices/ (accessed July 18, 2022).

84. Statista.com. Global Social Media Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/277115/social-media-traffic/ (accessed July 18, 2022).

85. Statista.com. Asia Social Media Traffic 2015–2021. (2022). Available online at: https://www.statista.com/statistics/27751/asia-socia-media-traffic/ (accessed July 18, 2022).

86. Statista.com. Asia Website Traffic 2015-2021. (2022). Available online at: https://www.statista.com/statistics/277557/global-asia-website-traffic/ (accessed July 18, 2022).

87. de Valle MK, Gallego-García M, Williamson P, Wade TD. Social media, body image, and the question of causation: Meta-analyses of experimental and longitudinal evidence. Body Image. (2021) 39:276–92. doi: 10.1016/j.bodyim.2021.10.001

88. Koetsier M. Current issues of social media and crisis communication. Soc Media Crises Commun. (2022) 20–32.

89. Paschke K, Austermann MI, Thomasius R. ICD-11-based assessment of social media use disorder in adolescents: development and validation of the social media use disorder scale for adolescents. Front Psychiatr. (2021) 12:661483. doi: 10.3389/fpsyt.2021.661483

90. Horwitz J. The Facebook Whistleblower, Frances Haugen, Says She Wants to Fix the Company, Not Harm It . New York, NY: The Wall Street Journal (2021). p. 3.

91. Raschke RL, Krishen AS, Kachroo P. Understanding the components of information privacy threats for location-based services. J Informat Syst. (2014) 28:227–42. doi: 10.2308/isys-50696

Keywords: bibliometric analysis, social media, social media addiction, problematic social media use, research trends

Citation: Pellegrino A, Stasi A and Bhatiasevi V (2022) Research trends in social media addiction and problematic social media use: A bibliometric analysis. Front. Psychiatry 13:1017506. doi: 10.3389/fpsyt.2022.1017506

Received: 12 August 2022; Accepted: 24 October 2022; Published: 10 November 2022.

Reviewed by:

Copyright © 2022 Pellegrino, Stasi and Bhatiasevi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Alfonso Pellegrino, alfonso.pellegrino@sasin.edu ; Veera Bhatiasevi, veera.bhatiasevi@mahidol.ac.th

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Times of San Diego

Local News and Opinion for San Diego

Supervisor Lawson-Remer to Propose Plan to Protect Youth from Social Media Addiction  

Share this:

• Click to share on Twitter (Opens in new window)
• Click to share on Facebook (Opens in new window)
• Click to share on Reddit (Opens in new window)
• Click to share on Pinterest (Opens in new window)
• Click to email a link to a friend (Opens in new window)
• Click to print (Opens in new window)

County Supervisor Terra Lawson-Remer Monday announced she will bring a proposal to the Board of Supervisors aimed at protecting young people from being victims of the “addictive algorithms” of social media.

The proposal, intended for next week’s board agenda, would direct the county’s legal team to explore filing and/or joining litigation against social media platforms for “their failure to protect youth mental health,” Lawson-Remer said in a statement.

In addition, it would include a resolution to support the U.S. surgeon general’s effort to put warning labels on social media platforms.

“Kids are being harmed by social media. It is affecting their mental and emotional well-being,” Lawson-Remer, the board’s vice chair, said.

“These platforms have knowingly developed addictive algorithms akin to the opioids manufactured by pharmaceutical companies. I want our county to legally intervene and throw our support behind the U.S. surgeon general’s recommendations.”

Last year, Surgeon General Vivek H. Murthy warned that social media are “contributing to our nation’s youth mental health crisis.”

The American Psychological Association followed suit, linking social media use to reduced well-being and mood disorders — chiefly depression and anxiety — among people between the ages of 10 and 25.

The APA reported that U.S. teens spend an average of five hours every day using the seven most popular social media apps, with Instagram, TikTok and YouTube accounting for 87% of their social media time.

According to Lawson-Remer’s office, “Youth are now exposed daily to strategically designed social media algorithms and features that capitalize on their vulnerabilities to drive engagement.”

City News Service contributed to this article.