research locale about social media addiction

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• 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

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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.

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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 .

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

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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.

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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

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• Longitudinal network analysis
• Psychological distress
• Social media addiction

BMC Psychiatry

ISSN: 1471-244X

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

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• 1 Sasin School of Management, Chulalongkorn University, Bangkok, Thailand.
• 2 Business Administration Division, Mahidol University International College, Mahidol University, Nakhon Pathom, Thailand.
• PMID: 36458122
• PMCID: PMC9707397
• DOI: 10.3389/fpsyt.2022.1017506

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.

Keywords: bibliometric analysis; problematic social media use; research trends; social media; social media addiction.

Copyright © 2022 Pellegrino, Stasi and Bhatiasevi.

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Developmental changes in brain function linked with addiction-like social media use two years later

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Jessica S Flannery, Kaitlyn Burnell, Seh-Joo Kwon, Nathan A Jorgensen, Mitchell J Prinstein, Kristen A Lindquist, Eva H Telzer, Developmental changes in brain function linked with addiction-like social media use two years later, Social Cognitive and Affective Neuroscience , Volume 19, Issue 1, 2024, nsae008, https://doi.org/10.1093/scan/nsae008

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Addiction-like social media use (ASMU) is widely reported among adolescents and is associated with depression and other negative health outcomes. We aimed to identify developmental trajectories of neural social feedback processing that are linked to higher levels of ASMU in later adolescence. Within a longitudinal design, 103 adolescents completed a social incentive delay task during 1–3 fMRI scans (6–9th grade), and a 4th self-report assessment of ASMU and depressive symptoms ∼2 years later (10–11th grade). We assessed ASMU effects on brain responsivity to positive social feedback across puberty and relationships between brain responsivity development, ASMU symptoms, and depressive symptoms while considering gender effects. Findings demonstrate decreasing responsivity, across puberty, in the ventral media prefrontal cortex, medial prefrontal cortex, posterior cingulate cortex, and right inferior frontal gyrus associated with higher ASMU symptoms over 2 years later. Significant moderated mediation models suggest that these pubertal decreases in brain responsivity are associated with increased ASMU symptoms which, among adolescent girls (but not boys), is in turn associated with increased depressive symptoms. Results suggest initial hyperresponsivity to positive social feedback, before puberty onset, and decreases in this response across development, may be risk factors for ASMU in later adolescence.

Social media serves many functions and is often a part of healthy adolescent development ( Leung, 2011 ; Deters and Mehl, 2013 ; Ellis et al ., 2020 ; Flannery et al ., 2023 ). However, addiction-like social media use (ASMU) is becoming increasingly reported ( Kuss et al ., 2014 ). Despite debate regarding the diagnostic utility of ASMU ( Panova and Carbonell, 2022 ), use of addiction terminology to discuss social media use behaviors has permeated popular culture ( Adorjan and Ricciardelli, 2021 ). Because of this, it is of interest to explore how behaviors reflecting craving for, and difficulty abstaining from, social media occur in adolescence. Accumulating work suggests that ASMU may share some of the same characteristics as other addictive disorders such as sustained preoccupation with cues, use for mood modification, tolerance following repeated use and withdrawal symptoms following abstinence ( Goldberg, 1996 ). Importantly, ASMU is not necessarily equated with the degree to which one uses social media, but instead captures the degree to which one feels a loss of control over their social media use or experiences negative effects (emotional or circumstantial) due to their use ( Baumer et al ., 2015 ; Turel et al ., 2018 ). As ASMU is characterized by maintained or increased social media use despite negative impacts on other aspects of life, and difficulty reducing use despite intensions to do so ( Baumer et al ., 2015 ), it is unsurprising that a quickly growing body of work indicates that ASMU may become disruptive to other aspects of life and have detrimental impacts on health and wellbeing ( Turel et al ., 2018 ). Indeed, research shows that ASMU symptoms are associated with depressive symptoms ( Robinson et al ., 2019 ), especially among adolescent girls ( Raudsepp and Kais, 2019 ). As adolescence is a period of vulnerability for both the onset of internalizing psychopathology ( McLaughlin and King, 2015 ) and ASMU symptoms ( Stavropoulos et al ., 2018 ), understanding links between ASMU and depressive symptoms across this period is vital.

The types of social feedback delivered via social media may be especially relevant to promoting addiction-like social media use behavior. Social feedback is both frequent and quantifiable (e.g. number of likes, number of followers) via social media. Such social feedback is also usually reinforcing and delivered on a variable ratio reinforcement schedule which is highly resistant to behavior extinction and is thus particularly addictive ( Greenfield, 2007 ). Social feedback delivery may be especially salient to adolescents, as adolescence is thought to be a period of heightened neurobiological and behavioral sensitivity to social stimuli, as well as prioritization of social connection and peer acceptance ( Somerville, 2013 ). Additionally, adolescence is characterized by a peak in reward sensitivity ( Lamm et al ., 2014 ) and reward seeking behaviors ( Galván, 2013 ), particularly in social contexts ( Smith et al ., 2015 ). This reward sensitivity is thought to stem from normative changes in brain structure and function that begin around the onset of puberty ( Padmanabhan et al ., 2011 ). Specifically, neuroimaging and preclinical work has repeatedly demonstrated links between higher levels of pubertal hormones and increased reward-related striatum activity among adolescents ( Forbes et al ., 2011 ; Op de Macks et al ., 2011 ). The onset of puberty may thus elicit normative developmental increases in neural responsivity to reinforcing social feedback ( Somerville, 2013 ; Smith et al ., 2015 ). Given these sensitivities, the continuous stream of highly salient and reinforcing social information dispensed via social media may have a uniquely powerful impact on adolescents.

Yet, all adolescents may not be equally prone to ASMU due to various individual predispositions, including possible biological vulnerabilities that increase sensitivity to social media cues. For example, individual differences in sensitivity to reinforcing social feedback may determine how adolescents navigate social media environments and the impact those environments have on adolescents’ mental health ( Sherman et al ., 2016 ). Specifically, adolescents who are more sensitive to social reward may be particularly apt to seek out social media incentives and thus may also be more susceptible to the provocation of continued use or even ASMU ( Sherman et al ., 2016 ). While development of adaptive incentive processing during adolescence is important for healthy development, hypersensitivity to rewards has been linked to externalizing and risk-taking behaviors ( Bjork and Pardini, 2015 ) and blunted reward sensitivity has been linked to depression ( O’Callaghan and Stringaris, 2019 ). Further, recent research has shown that, in the context of negative social experiences, neural hypersensitivity to social feedback is associated with an increased susceptibility to depressive symptoms ( Pagliaccio et al ., 2023 ) and externalizing behaviors ( Turpyn et al ., 2021 ) among adolescents. However, relationships between this neural hypersensitivity and ASMU across development are still not fully understood.

Prior work shows pubertal increases in neural responsivity to social feedback in brain regions typically involved in social processing including the medial prefrontal cortex (mPFC), posterior cingulate cortex (PCC), precuneus, inferior frontal cortex, fusiform gyrus and hippocampus ( Gunther Moor et al ., 2010 ). However, it is unclear whether individual differences in this development might constitute risk for future ASMU. We hypothesize that an initial hypersensitivity to such stimuli may drive some adolescents to increase their social media use more than others. However, recurrent over-exposure to social rewards via social media may, in turn, contribute to desensitization to social rewards across development. This hypothesis is based on prior observations of tolerance-build up after repeated administrations of an addictive drug ( Miller et al ., 1987 ; Albrecht et al ., 2007 ). Specifically, we hypothesize that adolescents reporting high ASMU in later adolescence may display initially heightened social reward responsivity that decreases over years of increasing social media use. This decrease may reflect desensitization to rewarding social feedback and a need for more social reward exposure to get the same reinforcing effects ( Griffiths et al ., 2014 ).

The current study investigates individual differences in pubertal trajectories of brain responsivity to positive social feedback across 6–9th grade, that are related to ASMU ∼2 years later. Interactive effects of gender identity were considered given prior work showing gender effects on social media use behaviors ( Nesi and Prinstein, 2015 ), ASMU prevalence ( Hawi and Samaha, 2019 ), and relationships between social media use and wellbeing ( Booker et al ., 2018 ). Further, we examine indirect effects of this differential brain function development on depressive symptoms in later adolescence through associations with ASMU symptoms, while again, considering gender effects. We hypothesize that initial hypersensitivity to positive social feedback, and longitudinal decreases across pubertal development, will be associated with higher ASMU symptoms in later adolescence. Further, we expect that, higher ASMU symptoms will, in turn, be associated depressive symptoms, particularly among girls.

Participants

Two cohorts of adolescent participants were recruited from three public middle schools across 2 years, as part of a larger longitudinal study of 6th and 7th grade students ( Supplementary Figure S1 ). The current study examines 103 adolescents that completed a social incentive delay (SID) task across 1–3 annual functional magnetic resonance imaging (fMRI) scan sessions (6–9th grade; 256 data points), as well as a self-report assessment 2.27 ± 0.21 years following their final fMRI scan (10–11th grade). The sample of 103 adolescents did not differ from the larger sample from which they were recruited on baseline gender, race/ethnicity, income, pubertal development or depressive symptoms. Of these 103 adolescents that had data at the final wave, 80 had data at the 1st timepoint, 90 had data at the 2nd timepoint and 86 had data at the 3rd timepoint ( Table 1 ).

Demographic information by timepoint

Note. Gender identity (% of total), age (mean ± standard deviation), and grade (% of total) are presented for each data collection timepoint. The multiple cohort structure of the study resulted in planned missing data across timepoints. Specifically, 62 of the total 103 participants had 3 fMRI timepoints, 29 had 2 and 12 had 1. All participants had a 4th self-report timepoint.

All participants provided informed consent/assent and were compensated for each completed session. The University’s Institutional Review Board approved all aspects of the study. Adolescent participants and their primary caregiver attended annual data collection sessions across three timepoints of a longitudinal fMRI study, at which adolescents completed an fMRI scan lasting ∼1.5 h. During each scan participants completed a social feedback task called the SID task, as well as an anatomical scan and four other tasks that are not the focus of the current manuscript. Following the scan, adolescents and caregivers completed several self-report measures. Adolescents and their caregivers returned about 2.3 years after their final fMRI scan timepoint and completed a 4th self-report assessment.

Pubertal development

Adolescents’ primary caregivers completed the Pubertal Development Scale (PDS) ( Petersen et al ., 1988 ) at each of the three fMRI scan timepoints. Scores ranged from (0): ‘puberty has not yet started’ to (3): ‘puberty is complete’ ( Icenogle et al ., 2017 ).

Depressive symptoms

Depressive symptoms were assessed at the final timepoint (∼2.3 years after the final fMRI scan) in 10–11th grade, using total scores on the unidimensional, 13-item Short Mood and Feelings Questionnaire (SMFQ; α = 0.93) ( Messer et al ., 1995 ), designed to measure depressive symptomology in children and adolescents aged 6- to 17-years old.

Addiction-like social media use symptoms

ASMU symptoms were also measured at the final timepoint with a novel 7-item questionnaire (α = 0.90) based on selected items from the Diagnostic Statistical Manual V (DSM-5) substance use disorder checklist. Example items include, ‘Does social media use ever get in the way of things you are supposed to be doing (e.g. sleep, exercise, schoolwork)?’, ‘Do you ever have a craving or strong desire to use social media?’, and ‘Have you ever been away from social media and felt like you were missing it too much to engage in normal day to day activities?’. Participants rated each symptom on a 4-point scale: (0): ‘I don’t have social media/not applicable’, (1): ‘never’, (2): ‘sometimes’, and (3): ‘often’. ASMU symptom endorsement was operationalized as responding ‘sometimes’ or ‘often’ for a given symptom and ranged from 0 to 7 symptoms endorsed. Participants were classified into severity levels based on the following DSM criteria: none: 0–1 symptom endorsed, mild: 2–3 symptoms endorsed, moderate: 4–5 symptoms endorsed and severe: 6 or more symptoms endorsed ( Hasin et al ., 2013 ). Participants meeting ‘severe’ criteria (i.e. 6 or more symptoms endorsed) were classified into a high ASMU group ( n  = 52) and all other participants, with 5 or less symptoms endorsed, were classified into a low ASMU group ( n  = 51).

While additional validation of this novel ASMU measure in other samples is warranted, the measure demonstrates evidence of important convergent validity with constructs that have been previously tested in the literature with other problematic and social media addiction/addiction-like social media use measures (with some minor deviations, as to be expected with variations across recruited samples). For example, past research has demonstrated correlations of r  = 0.17 and r  = −0.15 with neuroticism and conscientiousness, respectively ( Huang, 2022 ); the correlations observed in our data are similar, at 0.30 (neuroticism) and −0.26 (conscientiousness). In both our data and past research ( Huang, 2022 ), correlations with other Big Five traits are trivial and/or null ( r  < 0.12 in our study; r  < 0.10 in a meta-analysis conducted by Huang, 2022 ). Past meta-analytic work has also found a moderate correlation of r = 0.29 with depressive symptoms ( Cunningham et al ., 2021 ); the bivariate correlation observed in our data is quite similar at r  = 0.34. There is meta-analytic evidence that the correlation between the fear of missing out and social media addiction is quite high [ r  = 0.47; ( Yali et al ., 2021 )]; our data similarly demonstrates a high correlation ( r  = 0.68). Past research has found a moderate association between pathological social media use and poorer self-regulation [ r  = −0.26; ( Coyne et al ., 2017 )], and our data find similar evidence for moderate associations between ASMU and various facets of poorer self-control and greater impulsivity [ r ’s = 0.24–0.45; ( Marino et al ., 2018 )]. In addition to evidence of convergent validity, there was also evidence of discriminant validity. Past research has demonstrated that problematic Facebook use is only moderately associated with time spent on Facebook ( r  = 0.34); likewise, we find a moderate association between ASMU and frequency of checking social media ( r  = 0.42). Overall, these findings support the validity of our ASMU measure, given the comparable associations observed with various constructs in relation to past research utilizing alternative measures.

During MRI scanning, participants completed two 6.5 min runs of a SID task ( Figure 1 ) designed to measure neural sensitivity to anticipation (cue) and receipt (outcome) of positive social feedback (smiling face) and negative social feedback (scowling face). A total of 24 adolescent faces were used from the NIH faces dataset (12 females, 12 males). In this task, participants first see a cue indicating what type of trial will follow (happy, angry or neutral). Participants must press their right index finger as fast as they can after seeing the target to receive positive social feedback and avoid negative social feedback. To ensure sufficient and comparable exposure to all feedback types, task difficulty was individually and dynamically adapted based on prior performance by increasing or decreasing the target duration.

SID task . During MRI scanning, participants completed two 6.5 min runs of a SID task designed to measure neural sensitivity to anticipation (cue) and receipt (outcome) of social rewards (smiling face) and punishments (scowling face). Participants completed two 6.5 min runs consisting of 58 trials, resulting in a total of 116 trials (48 reward trials, 48 punishment trials and 20 neutral trials). In the task, participants see a cue (circle, square or diamond, 500 ms) indicating what type of trial will follow. Then, following a fixation cross (duration jittered ∼509–4249 ms), they see a target (white square, 160–500 ms). Participants are trained to press their right index finger as fast as they can after seeing the target, but not before. Following a delay (50 ms), participants receive social feedback (1450 ms) based on both the trial type and whether they pressed fast enough. The social feedback is photographs of adolescent faces taken from the NIH faces dataset ( Egger et al ., 2011 ). In the task, there were 24 faces shown (12 females, 12 males). Participants are explicitly told that the circle is a happy cue, the square is an angry cue and the diamond is a neutral cue, meaning if they press fast enough after seeing the happy cue (Reward cue), they will see a smiling face (Reward hit); if they press too slow after the happy cue, they will see blurred face (Reward miss). If they press fast enough after seeing the angry cue (Punishment cue), they will see a blurred face (Punishment hit); if they press too slow after the angry cue, they will see a scowling face (Punishment miss). Following the Neutral cue, they will see a blurred face whether they press fast enough (Neutral hit) or too slow (Neutral miss). To ensure sufficient exposure to all feedback types, task difficulty was individually and dynamically adapted based on prior performance by increasing or decreasing the target duration (starting at 300 ms) by 20 ms intervals unless reaching a minimum of 160 ms or maximum of 500 ms duration.

MRI data analysis

MRI data were collected on a Siemens Prisma MRI 3-Tesla scanner. The Supplementary materials contain information on neuroimaging data acquisition and preprocessing. Following preprocessing, SID functional data were entered into whole-brain participant-level general linear models (GLMs; SPM12) including eight event-related task regressors as impulse functions time-locked to stimulus onset and convolved with a hemodynamic response function. Task regressors included three anticipation conditions (happy, angry or neutral), two feedback conditions (i.e. hit and miss) for both the positive and negative conditions, and one neutral feedback condition. Six motion parameters were modeled as regressors of no interest. Whole-brain contrast images comparing conditions of interest were calculated for each participant. As we were interested in examining individual differences in how the brain responds to the reinforcing and social properties of social media, positive social feedback vs neutral feedback (i.e. smiling face feedback following a hit in positive conditions vs blurred out face feedback) was the primary contrast of interest for this study.

Statistical analysis

Next, we examined the effect of AMSU symptom endorsement on developmental brain responsivity to positive social feedback. To assess within-individual effects, we utilized multilevel statistical modeling that allowed for data missing at random. Within a linear multilevel framework (3dlmer; AFNI) ( Cox, 1996 ), modeling the random intercept and slope, we assessed a three-way GENDER × ASMU × PUBERTY cross-level interaction on the positive social feedback vs neutral feedback contrast. As ASMU research is still in its infancy, whether it is best to characterize ASMU as an all-or-none diagnosis or as a spectrum of severity is still an open question. Thus, in follow-up analyses, we also considered high (six or more symptoms endorsed; n  = 52) and low (five or less symptoms endorsed; n  = 51) ASMU groups. For full transparency, we report results from analyses in which ASMU symptoms are treated as a continuous variable and analyses in which ASMU symptoms are used to group participants. Given prior neuroimaging evidence of brain function during the SID task ( Martins et al ., 2021 ), we expected feedback delivery in the SID to recruit brain regions involved in social processing. As such, this analysis was conducted within a small volume corrected (SVC) brain mask (36 386 voxels) defined via NeuroSynth’s meta-analysis of the term ‘social’ ( Supplementary Figure S2 ). Three participants reporting nonbinary gender identities were not included in the analyses comparing effects of self-reported girl and boy identities ( N  = 100). For graphical examination and follow-up analyses, β-coefficients associated with the positive social feedback contrast were extracted by averaging across voxels within identified significant clusters/regions of interest (ROIs).

Next, we examined gender effects on ASMU symptom endorsement and depressive symptoms with independent samples t -tests and assessed gender effects on ASMU-group counts with a cross tabulation chi-squared test. Second, to assess both ASMU and gender effects on pubertal development across age, an ASMU-group × GENDER × AGE cross-level interaction was modeled while including random intercepts and slopes ( lmer , lme4 R package) ( Bates et al ., 2015 ). Third, we examined the effect of gender on associations between ASMU symptom endorsement and depressive symptoms with GENDER × ASMU symptom ANCOVAs. Finally, we formally tested relations between variables in a moderated mediation model (PROCESS v.4, model 14) ( Hayes, 2017 ) in which developmental changes in positive social feedback brain responsivity across pubertal development (slope coefficients associated with pubertal development) were associated with ASMU symptoms at the final timepoint that, in turn, were associated with depressive symptoms. Gender moderated the path from ASMU symptoms to depressive symptoms. In these models, positive social feedback slopes across pubertal development were entered as the predictor. Slope coefficients for each participant were extracted from linear multilevel models of pubertal development on positive social feedback β-coefficients from identified significant clusters/ROIs, in which the random intercept and slope of puberty as well as their correlation were modeled within participant ( Supplementary Figure S3 ).

Descriptives

Gender identity, race/ethnicity and annual household income did not significantly differ between high and low ASMU groups ( Table 2 , P ’s > 0.1). As expected, the high ASMU group had significantly higher depressive symptoms than the low ASMU group ( Table 2 ). We examined pubertal development trajectories in our sample of adolescents. A significant GENDER × AGE interaction on pubertal development was observed such that girls had higher pubertal development scale scores at an earlier age than boys (est. = 0.2, t [ 68.4 ] = 3.2, P  = 0.002). Nonsignificant ASMU group × GENDER × AGE interaction effects on pubertal development ( P ’s > 0.2) indicated that ASMU groups did not significantly differ in their pubertal development by age.

Demographic, socioeconomic and symptom information by ASMU group

Note. Gender identity (% of total) and race/ethnicity (% of total) were measured at each participant’s first timepoint (6th or 7th grade). Household annual income (% of total) and depressive symptoms (mean ± standard deviation) were measured at the final timepoint in 10–11th grade. ASMU symptoms (mean ± standard deviation) were also measured at the follow-up timepoint. ASMU symptoms endorsed are a count of the total number endorsed. ASMU-Group differences in gender identity, and race/ethnicity were assessed with cross tabulation chi-squared tests. ASMU-Group differences in household annual income were assessed with a Kruskal–Wallis test for ordinal variables which is similar to a one-way ANOVA but ranks of the data values are used in the test rather than the actual data points. ASMU-Group differences in depressive symptoms, ASMU symptoms were assessed with independent sample t -tests. * indicates significance at 0.05 level.

Changes in social feedback brain responsivity across puberty linked to ASMU

We examined an ASMU symptom endorsement × PUBERTY × GENDER interaction on positive social feedback brain responsivity. While we did not observe any significant effects for the three-way interaction, we did observe ASMU × PUBERTY interactions on positive social feedback responsivity in the PCC (130 voxels) and two clusters in the ventral media prefrontal cortex (vmPFC; 90 and 36 voxels; P voxel-level  < 0.005). However, these results did not pass cluster-extent thresholding (minimum cluster size = 213 voxels) determined via AFNI’s ACF 3dclustsim ( Cox et al ., 2017 ). In follow-up analyses, we also examined an ASMU-Group × PUBERTY × GENDER interaction on positive social feedback brain responsivity. While again we did not observe any significant effects for the three-way interaction, we did observe significant ASMU Group × PUBERTY interactions ( Figure 2 ; Supplementary Table S1 ) on positive social feedback responsivity in the vmPFC (358 voxels), mPFC (393 voxels), PCC (295 voxels) and right inferior frontal gyrus (rIFG; 226 voxels). To probe this interaction, β coefficients were averaged across voxels in each of these four significant clusters/ROIs. In all four clusters, adolescents in the low ASMU group displayed relatively lower responsivity to positive social feedback before puberty onset that increased with pubertal development. In contrast, adolescents in the high ASMU group displayed hyper-responsivity before puberty onset that decreased with pubertal development. These results did not significantly change after removing brain responsivity outliers. However, given potential for spurious detection of disordinal interactions with whole-brain ANOVAs, we caution against over-interpretation of differences in AMSU-group trajectories ( Chavez and Wagner, 2017 ). While no significant interactive effects of GENDER were detected, we observed a significant GENDER main effect on positive social feedback responsivity in the right superior temporal sulcus (rSTS 350 voxels) such that girls displayed increased rSTS responsivity compared to boys. No significant PUBERTY main effects or PUBERTY × GENDER interactions were observed. In the follow-up exploratory analyses, we also assessed brain responsivity to anticipation of positive social feedback vs anticipation of neutral feedback but did not observe any significant effects of interest.

Changes in social feedback brain responsivity across puberty linked to ASMU . Significant ASMU Group × PUBERTY interactions were observed in the ventral media prefrontal cortex ( A . vmPFC; 358 voxels), medial prefrontal cortex ( B . mPFC; 393 voxels), posterior cingulate cortex ( C . PCC; 295 voxels) and right inferior frontal gyrus ( D . rIFG; 226 voxels) when controlling for GENDER effects. In all four significant clusters, adolescents in the low ASMU group displayed relatively lower responsivity to positive social feedback before puberty onset that increased with pubertal development, whereas adolescents in the high ASMU group displayed hyper-responsivity before puberty onset that decreased with pubertal development.

Gender effects

At the final timepoint in 10–11th grade, we did not observe significant gender differences in ASMU symptom endorsement ( t [ 98 ] = 1.1, P  = 0.2) or ASMU-group counts ( χ 2 [ 1, 100 ] = 2.7, P  = 0.3). However, girls reported significantly higher depressive symptoms ( t [ 98 ] = 2.9, P  = 0.004, Cohen’s d  = 0.58) than boys. ( Figure 3A ). Further, higher ASMU symptom endorsement was significantly associated with higher depressive symptoms among girls, but not among boys ( F [ 100 ] = 6.3, P  = 0.014, η p 2  = 0.06; Figure 3B ).

Addiction-like social media use symptoms mediate the effect of vmPFC change across puberty on depressive symptoms among girls . ( A ) Significantly higher depressive symptoms in 10–11th grade, among adolescent girls compared to boys. ( B ) Higher ASMU symptoms were significantly associated with higher depressive symptoms among girls but not among boys. ( C ) A significant moderated mediation model (PROCESS v.4, model 14) demonstrated that decreasing ventral media prefrontal cortex (vmPFC) responsivity across puberty was related to increased ASMU symptoms ∼2.3 years later (10th and 11th grade) which, among adolescent girls (but not boys), was in turn, associated with increased depressive symptoms.

ASMU symptoms differentially mediate the effect of brain responsivity development on depressive symptoms among adolescent girls and boys

A significant moderated mediation model [Index of Moderated Mediation: 2.1, 95% confidence interval (CI) = (0.1, 4.8)] indicated that, higher ASMU symptom endorsement mediated the association between decreasing vmPFC responsivity across pubertal development and increased depressive symptoms over two years later; this effect was only significant amongst girls [indirect effect among girls: ab = −2.0, 95% CI = (−4.3, −0.1); Figure 3C ]. Specifically, while decreases in vmPFC responsivity to positive social feedback across puberty was not directly related depressive symptoms in later adolescence, it was related to increased ASMU symptom endorsement which was, in turn, related to higher depressive symptoms, but only among girls. This same model was also significant for the rIFG [Index of Moderated Mediation: 6.8, 95% CI = (2.3, 12.8); indirect effect among girls: ab = −5.0, 95% CI = (−9.8, −1.6); Supplementary Figure S4A ], but was not for the PCC [Index of Moderated Mediation: 3.0, 95% CI = (−1.0, 7.3); Supplementary Figure S4B ] nor the mPFC [Index of Moderated Mediation: −8.4, 95% CI = (−77.1, 70.8); Supplementary Figure S4C ]. Follow-up analyses indicated that the unmoderated mediation was also not significant for the PCC nor the mPFC.

Our results identify differential changes in social feedback brain responsivity across puberty in four brain regions (i.e. vmPFC, rIFG, mPFC and PCC) among adolescents that subsequently reported more addiction-like social media use. Prior work has demonstrated developmental changes (e.g. childhood to adolescence, and early adolescence to late adolescence) in responsivity to positive social outcomes ( Somerville, 2013 ; Smith et al ., 2015 ). For example, striatal and anterior cingulate cortex activity to positive social feedback anticipation increases from childhood to adolescence ( Gunther Moor et al ., 2010 ). As such, we expected brain responsivity to social feedback to increase with pubertal development. However, across the full sample this was not observed. Instead, we observed that this trajectory diverged for high and low ASMU groups. While the low ASMU group displayed expected increases in positive social feedback responsivity across pubertal development, adolescents in the high ASMU group displayed elevated social feedback responsivity in the vmPFC, rIFG, mPFC and PCC before puberty onset that then decreased with pubertal development.

These results could suggest that some adolescents with premature elevations in neural social feedback sensitivity may initially be more sensitive to the delivery of social feedback via media. However, observed pubertal decreases in social feedback vmPFC, rIFG, PCC and mPFC responsivity may reflect desensitization to such feedback, possibly through mechanisms similar to those driving tolerance-build up after repeated administrations of an addictive drug. As we did not have data on participants’ amount of social media exposure over pubertal development this hypothesis could not be further explored in this sample. Nonetheless, our findings indicate that developmental changes in brain function previously implicated in social information processing may be associated with individual differences in ASMU susceptibility. Such information could help target prevention efforts aiming to mitigate maladaptive social media use and its influence on adolescent mental health.

ASMU symptoms mediate the effect of brain responsivity development on depressive symptoms

Prior work suggests that increased brain responsivity to social feedback may impact adolescent mental health through effects on their social experiences. Specifically, anterior insula, cingulate, amygdala and striatum responsivity to social feedback among young adolescents (12-to 14-years old) impacts associations between peer interpersonal stress and depression years later ( Pagliaccio et al ., 2023 ). Further, prior findings from our research team similarly demonstrate that heightened amygdala, and striatum activity to social feedback among young adolescents (11- to 14-years old) impacts associations between family conflict and externalizing behavior ( Turpyn et al ., 2021 ). Our current findings extend this prior work showing that elevations in neural responsivity to social feedback in early adolescence and subsequent developmental decreases into later adolescence may also impact social media use behaviors, and that this impact may increase risk for depressive symptoms.

Further, our results highlight the importance of vmPFC and rIFG social feedback responsivity development. While all identified brain regions are implicated in social information processing ( Spreng and Andrews-Hanna, 2015 ), the vmPFC, in particular, has been distinguished from other regions for its role in processing positively valanced or rewarding social information ( van den Bos et al ., 2007 ). Further, other work has suggested that the vmPFC and PCC may both be involved in processing self-referential social information (like social feedback), while more dorsal medial frontal regions are involved in thinking about the mental states of others ( Wagner et al ., 2012 ). Additionally, while the rIFC is known to be recruited when processing affective social information, evidence suggests that it may be specifically involved when reappraising this information ( Grecucci et al ., 2013 ). Encoding valance, affective information and referencing the self are likely all important when processing social feedback delivered on social media (e.g. likes or comments). Taken together, these results suggest that development of vmPFC and rIFG responsivity to social feedback are likely important targets for understanding how adolescents process social feedback delivered via social media and the impact it has on their media use behaviors and mental health.

Gender differences in mediating effect of ASMU symptoms on relationship between brain responsivity development and depressive symptoms

Our results suggest gender effects in ASMU consequences by demonstrating a path through which divergent developmental trajectories of vmPFC and rIFG function may indirectly lead to depressive symptoms among girls through associations with more ASMU symptoms. This finding corresponds with prior work indicating higher social media use at age 10 is associated with declines in well-being into early and mid-adolescence for girls but not for boys ( Booker et al ., 2018 ). One possible explanation may be, as proposed by social role theory ( Eagly and Wood, 2012 ), girls are socialized to value social connections and social standing more so than boys are. If this is the case, it is perhaps unsurprising that girls’ neural sensitivity to social feedback might carry more influence on their mental wellbeing in instances of regular over-exposure to such information on social media.

Another possible explanation of these results may be that gender is associated with differential susceptibility to different types of media use (i.e. social media vs non-social digital media) and the respective consequences. Emergent meta-analytic research suggests that ASMU may be more prevalent among women while addiction-like internet gaming is more prevalent among men ( Su et al ., 2020 ). Social media may particularly foster certain maladaptive use experiences among adolescents, which are not as prevalent when using other non-social media, including social comparison, fear of missing out, feedback seeking, and body image insecurities ( Nesi and Prinstein, 2015 ). Therefore, adolescent girls may be especially at risk. Future work examining various media types and their unique impacts on mental health could more fully depict gender-based susceptibility to ASMU.

Limitations

Certain limitations should be considered. First, additional validation of the ASMU measure in other samples is warranted and as social norms surrounding digital media use shift, what constitutes problematic digital media behavior will also likely need to shift. Second, several statistical considerations should be noted regarding our observed ASMU group by puberty interaction on social reward brain responsivity. First, while we observed significant interaction effects when dichotomizing the ASMU variable, results did not pass cluster-extent threshold corrections before dichotomizing. This discrepancy may be related to dichotomization reducing error in the measurement of the ASMU construct and thus increasing power. Further, we also note the potential for spurious interactions in whole-brain ANOVAs and thus the nature of each AMSU group’s developmental trajectory should be interpreted with caution ( Chavez and Wagner, 2017 ). Third, while the PDS is a widely used indicator of pubertal maturation that corresponds with other measures of puberty, including physician Tanner ratings and hormone levels ( Herting et al ., 2021 ), puberty is a highly individualized process and future work examining bias in caregiver-reports is warranted. Finally, information on social media exposure and use behaviors before and across pubertal development was not available. As such, we could not explore hypotheses regarding neural desensitization to social feedback due to accumulating exposure via social media use.

Our findings suggest that before puberty onset, hyper-responsivity to positive social feedback, in four brain regions associated social information processing (vmPFC, rIFG, mPFC and PCC), may represent a risk factor for ASMU in later adolescence. In contrast, decreases in this neural response over pubertal development could suggest atypical development of social feedback processing that is also linked with ASMU. Results suggest that these developmental differences in social feedback brain responsivity are associated with depressive symptoms among adolescent girls through their relationship with increased ASMU symptom endorsement in later adolescence. Taken together, this study identifies developmental individual differences (i.e. brain responsivity to social feedback and gender identity) that may be important for understanding ASMU susceptibility and related mental health outcomes.

Supplementary data is available at SCAN online.

The authors have released all code associated with this manuscript. Code is available on GitHub https://github.com/Flanneryg3/ASMU?ProjectCode and group-level statistical brain maps are stored in the following NeuroVault collection: https://identifiers.org/neurovault.collection:14537 .

Conceptualization: E.H.T. and J.S.F.; data curation: J.S.F., K.B., S.K., and N.A.J.; formal analysis: J.S.F. and K.B.; visualization and original draft writing: J.S.F.; draft review and editing: K.B., S.K., N.A.J., M.J.P., K.A.L., and E.H.T.; funding acquisition: E.H.T., K.A.L., and M.J.P.; supervision: E.H.T. and K.A.L.

Primary support for this project was provided by grants from the National Institute on Drug Abuse (NIDA) [F32DA057876] to J.S.F., [R01DA051127] to E.H.T. and K.A.L., and [R01DA039923] to E.H.T. and the Winston Family Foundation. Funders were not involved in the design and conduct of the study, collection, management, analysis, and the interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication.

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Dr. Telzer and Dr. Prinstein reported receiving private research funds from the Winston Family Foundation during the conduct of the study. The Winston Family Foundation had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review or approval of the manuscript; and decision to submit the manuscript for publication.

Adorjan   M. , Ricciardelli   R. ( 2021 ). Smartphone and social media addiction: exploring the perceptions and experiences of Canadian teenagers . Canadian Review of Sociology/Revue Canadienne de Sociologie , 58 ( 1 ), 45 – 64 .

Google Scholar

Albrecht   U. , Kirschner   N.E. , Grüsser   S.M. ( 2007 ). Diagnostic instruments for behavioural addiction: an overview . GMS Psycho-Social Medicine , 4 , 1 – 11 .

Bates   D. , Mächler   M. , Bolker   B. , Walker   S. ( 2015 ). Fitting linear mixed-effects models Usinglme4 . Journal of Statistical Software , 67 ( 1 ), 1 – 48 .

Baumer   E.P.S. , Guha   S. , Quan   E. , Mimno   D. , Gay   G.K. ( 2015 ). Missing photos, suffering withdrawal, or finding freedom? How experiences of social media non-use influence the likelihood of reversion . Social Media + Society , 1 ( 2 ), 1 – 14 .

Bjork   J.M. , Pardini   D.A. ( 2015 ). Who are those “risk-taking adolescents”? Individual differences in developmental neuroimaging research . Developmental Cognitive Neuroscience , 11 , 56 – 64 .

Booker   C.L. , Kelly   Y.J. , Sacker   A. ( 2018 ). Gender differences in the associations between age trends of social media interaction and well-being among 10-15 year olds in the UK . BMC Public Health . , 18 ( 1 ), 1 – 12 .

Chavez   R.S. , Wagner   D.D. ( 2017 ). Mass univariate testing biases the detection of interaction effects in whole-brain analysis of variance . bioRxiv .

Cox   R.W. ( 1996 ). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages . Computers and Biomedical Research, an International Journal , 29 ( 3 ), 162 – 73 .

Cox   R.W. , Chen   G. , Glen   D.R. , Reynolds   R.C. , Taylor   P.A. ( 2017 ). FMRI clustering in AFNI: false-positive rates redux . Brain Connectivity , 7 ( 3 ), 152 – 71 .

Coyne   S.M. , Nelson   D.A. , Carroll   J.S. , et al.  ( 2017 ). Relational aggression and marital quality: five-year longitudinal study . Journal of Family Psychology , 31 ( 3 ), 282 – 93 .

Cunningham   S. , Hudson   C.C. , Harkness   K. ( 2021 ). Social media and depression symptoms: A meta-analysisa . Research on Child and Adolescent Psychopathology , 49 ( 2 ), 241 – 53 .

Deters Große   F. , Mehl   M.R. ( 2013 ). Does posting facebook status updates increase or decrease loneliness? An online social networking experiment . Social Psychological and Personality Science , 4 ( 5 ), 579 – 86 .

Eagly   A.H. Wood   W. ( 2012 ). Social role theory. In: Van Lange , P. , Kruglanski , A. , Higgins , E., editors. Handbook of Theories of Social Psychology . Thousand Oaks, CA, USA : SAGE Publications Ltd , 458 – 76 .

Google Preview

Egger   H.L. , Pine   D.S. , Nelson   E. , et al.  ( 2011 ). The NIMH Child Emotional Faces Picture Set (NIMH-ChEFS): a new set of children’s facial emotion stimuli: child emotional faces stimuli . International Journal of Methods in Psychiatric Research , 20 ( 3 ), 145 – 56 .

Ellis   W.E. , Dumas   T.M. , Forbes   L.M. ( 2020 ). Physically isolated but socially connected: psychological adjustment and stress among adolescents during the initial COVID-19 crisis . Canadian Journal of Behavioural Science/Revue Canadienne Des Sciences Du Comportement , 52 ( 3 ), 177 – 87 .

Flannery   J.S. Maza   M.T. Kilic   Z. Telzer   E.H. ( 2023 ). Chapter Nine—Cascading bidirectional influences of digital media use and mental health in adolescence. In: Tamis-Lemonda , C.S. , Lockman , J.J. , editors. Advances in Child Development and Behavior , Vol. 64 , Amsterdam, Netherlands : Elsevier Ltd .

Forbes   E.E. , Phillips   M.L. , Silk   J.S. , Ryan   N.D. , Dahl   R.E. ( 2011 ). Neural systems of threat processing in adolescents: role of pubertal maturation and relation to measures of negative affect . Developmental Neuropsychology , 36 ( 4 ), 429 – 52 .

Galván   A. ( 2013 ). The teenage brain: sensitivity to rewards . Current Directions in Psychological Science , 22 ( 2 ), 88 – 93 .

Goldberg   I. ( 1996 ). Internet addictive disorder (IAD) diagnostic criteria . CyberPsychol. Behavior , 3(4) , 403 – 12 .

Grecucci   A. , Giorgetta   C. , Bonini   N. , Sanfey   A. ( 2013 ). Reappraising social emotions: the role of inferior frontal gyrus, temporo-parietal junction and insula in interpersonal emotion regulation . Frontiers in Human Neuroscience , 7 , 523.

Greenfield   D. ( 2007 ). The addictive properties of internet usage. Internet Addiction: A Handbook and Guide to Evaluation and Treatment . Hoboken, NJ, USA : John Wiley & Sons, Ltd , 133 – 53 .

Griffiths   M.D. Kuss   D.J. Demetrovics   Z. ( 2014 ). Chapter 6 - social networking addiction: An overview of preliminary findings. In: Rosenberg , K.P. , Feder , L.C. , editors. Behavioral Addictions . Cambridge, MA, USA : Academic Press . 119 – 41 .

Gunther Moor   B. , van Leijenhorst   L. , Rombouts   S.A.R.B. , Crone   E.A. , Van der Molen   M.W. ( 2010 ). Do you like me? Neural correlates of social evaluation and developmental trajectories . Social Neuroscience , 5 ( 5–6 ), 461 – 82 .

Hasin   D.S. , O’Brien   C.P. , Auriacombe   M. , et al.  ( 2013 ). DSM-5 criteria for substance use disorders: recommendations and rationale . The American Journal of Psychiatry , 170 ( 8 ), 834 – 51 .

Hawi   N. , Samaha   M. ( 2019 ). Identifying commonalities and differences in personality characteristics of Internet and social media addiction profiles: traits, self-esteem, and self-construal . Behaviour & Information Technology , 38 ( 2 ), 110 – 9 .

Hayes   A.F. ( 2017 ). Introduction to Mediation, Moderation, and Conditional Process Analysis, Second Edition: A Regression-Based Approach . New York, NY, USA : Guilford Publications .

Herting   M.M. , Uban   K.A. , Gonzalez   M.R. , et al.  ( 2021 ). Correspondence between perceived pubertal development and hormone levels in 9-10 year-olds from the adolescent brain cognitive development study . Frontiers in Endocrinology , 11 , 549928.

Huang   C. ( 2022 ). A meta-analysis of the problematic social media use and mental health . International Journal of Social Psychiatry , 68 ( 1 ), 12 – 33 .

Icenogle   G. , Steinberg   L. , Olino   T.M. , et al.  ( 2017 ). Puberty predicts approach but not avoidance on the iowa gambling task in a multinational sample . Child Development , 88 ( 5 ), 1598 – 614 .

Kuss   D. , Griffiths   M. , Karila   L. , Billieux   J. ( 2014 ). Internet addiction: a systematic review of epidemiological research for the last decade . Current Pharmaceutical Design , 20 ( 25 ), 4026 – 52 .

Lamm   C. , Benson   B.E. , Guyer   A.E. , et al.  ( 2014 ). Longitudinal study of striatal activation to reward and loss anticipation from mid-adolescence into late adolescence/early adulthood . Brain and Cognition , 89 , 51 – 60 .

Leung   L. ( 2011 ). Loneliness, social support, and preference for online social interaction: the mediating effects of identity experimentation online among children and adolescents . Chinese Journal of Communication , 4 ( 4 ), 381 – 99 .

Marino   C. , Gini   G. , Vieno   A. , Spada   M.M. ( 2018 ). The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: a systematic review and meta-analysis . Journal of Affective Disorders , 226 , 274 – 81 .

Martins   D. , Rademacher   L. , Gabay   A.S. , et al.  ( 2021 ). Mapping social reward and punishment processing in the human brain: a voxel-based meta-analysis of neuroimaging findings using the social incentive delay task . Neuroscience and Biobehavioral Reviews , 122 , 1 – 17 .

McLaughlin   K.A. , King   K. ( 2015 ). Developmental trajectories of anxiety and depression in early adolescence . Journal of Abnormal Child Psychology , 43 ( 2 ), 311 – 23 .

Messer   S.C. , Angold   A. , Costello   E.J. , et al.  ( 1995 ). Development of a short questionnaire for use in epidemiological studies of depression in children and adolescents: factor composition and structure across development . International Journal of Methods in Psychiatric Research , 5 , 251 – 62 .

Miller   N.S. , Dackis   C.A. , Gold   M.S. ( 1987 ). The relationship of addiction, tolerance, and dependence to alcohol and drugs: a neurochemical approach . Journal of Substance Abuse Treatment , 4 ( 3 ), 197 – 207 .

Nesi   J. , Prinstein   M.J. ( 2015 ). Using social media for social comparison and feedback-seeking: gender and popularity moderate associations with depressive symptoms . Journal of Abnormal Child Psychology , 43 ( 8 ), 1427 – 38 .

O’Callaghan   G. , Stringaris   A. ( 2019 ). Reward processing in adolescent depression across neuroimaging modalities . Zeitschrift Fur Kinder- Und Jugendpsychiatrie Und Psychotherapie , 47 ( 6 ), 535 – 41 .

Op de Macks   Z.A. , Moor   B.G. , Overgaauw   S. , Güroğlu   B. , Dahl   R.E. , Crone   E.A. ( 2011 ). Testosterone levels correspond with increased ventral striatum activation in response to monetary rewards in adolescents . Developmental Cognitive Neuroscience , 1 ( 4 ), 506 – 16 .

Padmanabhan   A. , Geier   C.F. , Ordaz   S.J. , Teslovich   T. , Luna   B. ( 2011 ). Developmental changes in brain function underlying the influence of reward processing on inhibitory control . Developmental Cognitive Neuroscience , 1 ( 4 ), 517 – 29 .

Pagliaccio   D. , Kumar   P. , Kamath   R.A. , Pizzagalli   D.A. , Auerbach   R.P. ( 2023 ). Neural sensitivity to peer feedback and depression symptoms in adolescents: a 2-year multiwave longitudinal study . Journal of Child Psychology and Psychiatry , 64 ( 2 ), 254 – 64 .

Panova   T. Carbonell   X. ( 2022 ). Social media addiction. In: Pontes , H.M. , editor. Behavioral Addictions: Conceptual, Clinical, Assessment, and Treatment Approaches . New York, NY, USA : Springer International Publishing . 69 – 95 .

Petersen   A.C. , Crockett   L. , Richards   M. , Boxer   A. ( 1988 ). A self-report measure of pubertal status: reliability, validity, and initial norms . Journal of Youth and Adolescence , 17 ( 2 ), 117 – 33 .

Raudsepp   L. , Kais   K. ( 2019 ). Longitudinal associations between problematic social media use and depressive symptoms in adolescent girls . Preventive Medicine Reports , 15 , 100925.

Robinson   A. , Bonnette   A. , Howard   K. , et al.  ( 2019 ). Social comparisons, social media addiction, and social interaction: an examination of specific social media behaviors related to major depressive disorder in a millennial population . Journal of Applied Biobehavioral Research , 24 ( 1 ), e12158.

Sherman   L.E. , Payton   A.A. , Hernandez   L.M. , Greenfield   P.M. , Dapretto   M. ( 2016 ). The power of the like in adolescence: effects of peer influence on neural and behavioral responses to social media . Psychological Science , 27 ( 7 ), 1027 – 35 .

Smith   A.R. , Steinberg   L. , Strang   N. , Chein   J. ( 2015 ). Age differences in the impact of peers on adolescents’ and adults’ neural response to reward . Developmental Cognitive Neuroscience , 11 , 75 – 82 .

Somerville   L.H. ( 2013 ). The teenage brain: sensitivity to social evaluation . Current Directions in Psychological Science , 22 ( 2 ), 121 – 7 .

Spreng   R.N. , Andrews-Hanna   J.R. ( 2015 ). The Default Network and Social Cognition . Brain Mapping: An Encyclopedic Reference , 3 , 165 – 9 .

Stavropoulos   V. , Griffiths   M.D. , Burleigh   T.L. , Kuss   D.J. , Doh   Y.Y. , Gomez   R. ( 2018 ). Flow on the Internet: a longitudinal study of Internet addiction symptoms during adolescence . Behaviour & Information Technology , 37 ( 2 ), 159 – 72 .

Su   W. , Han   X. , Yu   H. , Wu   Y. , Potenza   M.N. ( 2020 ). Do men become addicted to internet gaming and women to social media? A meta-analysis examining gender-related differences in specific internet addiction . Computers in Human Behavior , 113 , 106480.

Turel   O. , Brevers   D. , Bechara   A. ( 2018 ). Time distortion when users at-risk for social media addiction engage in non-social media tasks . Journal of Psychiatric Research , 97 , 84 – 8 .

Turpyn   C.C. , Jorgensen   N.A. , Prinstein   M.J. , Lindquist   K.A. , Telzer   E.H. ( 2021 ). Social neural sensitivity as a susceptibility marker to family context in predicting adolescent externalizing behavior . Developmental Cognitive Neuroscience , 51 , 100993.

van den Bos   W. , McClure   S.M. , Harris   L.T. , Fiske   S.T. , Cohen   J.D. ( 2007 ). Dissociating affective evaluation and social cognitive processes in the ventral medial prefrontal cortex . Cognitive, Affective, & Behavioral Neuroscience , 7 ( 4 ), 337 – 46 .

Wagner   D.D. , Haxby   J.V. , Heatherton   T.F. ( 2012 ). The representation of self and person knowledge in the medial prefrontal cortex . Wiley Interdisciplinary Reviews Cognitive Science , 3 ( 4 ), 451 – 70 .

Yali   Z. , Sen   L.I. , Guoliang   Y.U. ( 2021 ). The relationship between social media use and fear of missing out: A meta-analysis . Acta Psychologica Sinica , 53 ( 3 ), 273 – 90 .

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Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator

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• Volume 21 , pages 2037–2051, ( 2023 )

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• Begum Satici   ORCID: orcid.org/0000-0003-2161-782X 1 ,
• Ahmet Rifat Kayis   ORCID: orcid.org/0000-0003-4642-7766 2 &
• Mark D. Griffiths   ORCID: orcid.org/0000-0001-8880-6524 3  

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Social media use has become part of daily life for many people. Earlier research showed that problematic social media use is associated with psychological distress and relationship satisfaction. The aim of the present study was to examine the mediating role of psychological distress in the relationship between social media addiction (SMA) and romantic relationship satisfaction (RS). Participants comprised 334 undergraduates from four mid-sized universities in Turkey who completed an offline survey. The survey included the Relationship Assessment Scale, the Social Media Disorder Scale, and the Depression Anxiety and Stress Scale. According to the results, there were significant correlations between all variables. The results also indicated that depression, anxiety, and stress partially mediated the impact of SMA on RS. Moreover, utilizing the bootstrapping procedure the study found significant associations between SMA and RS via psychological distress. Consequently, reducing social media use may help couples deal with romantic relationship dissatisfaction, thereby mitigating their depression, anxiety, and stress.

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Establishing social relationships is one of the basic needs of human beings (Heaney & Israel, 2008 ). How this basic need is met can vary greatly. In particular, technological developments, such as computers, the Internet, and smartphones have created new ways for people to communicate with each other. One of the most successful new means of communication is through social media. Social media involves many different communication (i.e., social networking) platforms. Among the most popular are platforms in Western countries are Facebook, Twitter, Instagram, and YouTube. These sites, which are accessed via the Internet, provide many opportunities for communication, such as voice and video messaging, photograph and video sharing, and creating profiles, through which individuals can introduce themselves and make connections with others.

The communication opportunities brought about by social networking sites (SNSs) allow for the development of social relationships (Fuchs, 2017 ; Hazar, 2011 ; Valentini, 2015 ). In addition, social media is used for a wider variety of purposes, including obtaining information, communicating, entertainment, playing games, and sharing photos, videos, and music (Griffiths, 2012 ). However, excessive use of social media including SNSs can cause negative effects (Griffiths, 2013 ; van den Eijnden et al., 2016 ). This phenomenon, which is sometimes referred as “social media addiction,” is defined as the irrational and excessive use of social media at a level that negatively affects the daily life of the user (Griffiths, 2012 ). When social media use reaches the level of addiction, it can prevent the establishment of real, face-to-face social relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ). When general characteristics of social media addiction have been examined, it has been found that individuals tend to have restless thoughts concerning the urges and craving to be on social media, lose their self-control over their use of social media, spend excessive amounts of time staying on (or thinking about) social media which in turn lead to negative impacts on their relationships with their families and friends, and compromise their occupation and/or education (Andreassen et al., 2012 ; Griffiths et al., 2014 ). Therefore, examining social media addiction in terms of its effect on human relationships and mental health is an important pursuit.

Theoretical Framework

Social media addiction and relationship satisfaction.

Research into the effects of social media addiction on romantic relationships has increased (Abbasi, 2019a ; Demircioğlu & Köse, 2018 ). The literature suggests that social media addiction negatively affects romantic relationships due to its tendency to create jealousy and suspicion and facilitate deception between married couples and committed partners (Abbasi, 2019b ). Additionally, problematic social media use can hinder the development of face-to-face relationships (Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Pollet et al., 2011 ; Young, 2019 ). Therefore, it is possible that some couples’ relationships may become disrupted and that dissatisfaction may be experienced. In some cases, not only has social media use decreased the amount of relationships that individuals have in person, but it has also markedly impaired the quality of the time spent together. Therefore, it can be concluded that some couples may experience relationship dissatisfaction.

Similarly, social media addiction can result in low relationship satisfaction due to the existence of online alternative centers of attraction and investments of time and emotion outside the bilateral relationship in individuals aged between 18 and 73 years (Abbasi, 2019a ). In addition, social media addiction has also been associated with physical and emotional infidelity, romantic separation, decline in the quality of romantic relationships, and relationship dissatisfaction (e.g., Abbasi, 2019a , b ; Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ). Therefore, these aforementioned findings indicate that social media addiction negatively affects relationship satisfaction.

Social Media Addiction and Psychological Distress

One of the most important consequences of social media addiction is the mental health of individuals. When social media use reaches the level of addiction, it can create stress and negatively affect mental health rather than being a method of healthy coping. This occurs because social media addiction triggers social media fatigue and, as a result, individuals may experience anxiety and depression (Dhir et al., 2018 ). Social media users may use social media as a means of diversion in order to cope with stress (van den Eijnden et al., 2016 ). However, social media addicts give a lower priority to hobbies, daily routines, and close relationships (Tutgun-Ünal & Deniz, 2015 ) which in turn lead to problems with daily functioning, completion of tasks, and relationship maintenance. This puts such individuals at risk for experiencing negative physical and psychological health.

In fact, some research has claimed that social media addiction triggers psychological distress factors, such as depression, anxiety (Woods & Scott, 2016 ), and stress (Larcombe et al., 2016 ). In addition, a meta-analysis synthesizing the findings of 13 studies found that social media addiction may increase depression, anxiety, and stress levels (Keles et al., 2020 ). In both meta-analyses and cross-sectional studies, it has been found that social media addiction can increase psychological distress (e.g., Hou et al., 2019 ; Keles et al., 2020 ; Marino et al., 2018 ; Meena et al., 2015 ). In sum, these findings consistently associate social media addiction with psychological distress.

Psychological Distress and Relationship Satisfaction

Individuals experiencing psychological discomfort often have non-functional communication styles characterized by highly negative behaviors, such as criticism, complaining, hostility, defensiveness, and tendency to end relationships. They also experience problems actively listening to others (Fincham et al., 2018 ). In this respect, psychological distress prevents healthy communication in relationships, and a lack of healthy communication may cause conflicts that can embitter psychological distress between couples. Such a situation can continue in a cyclical manner that prevents relationship satisfaction. In romantic relationships, couples are supposed to fulfill their partners’ emotional needs (Willard, 2011 ). When individuals have psychological problems due to social media addiction, they will ignore their partner’s emotional needs because they would be trying to deal with their own problems, which, in turn, may lead to lower relationship satisfaction.

When psychological distress and romantic relationship satisfaction are examined, it can be seen that much psychological distress, such as major depression, panic disorder, social phobia, general anxiety disorder, post-traumatic stress disorder, and mood disorder, positively predict relationship dissatisfaction (Whisman, 1999 ). On the other hand, it can also be seen that individuals who are sensitive to negative affect in romantic relationships and who can successfully stop these emotions early on and cope with their feelings are satisfied with their relationships (Fincham et al., 2018 ).

Couples who have high levels of stress are reported to experience less satisfaction in their relationships (Bodenmann et al., 2007 ). In addition, it is known that depression negatively predicts relationship satisfaction (Cramer, 2004a , b ; Tolpin et al., 2006 ). Therefore, it appears that psychological distress negatively affects relationship satisfaction.

The Present Study

The prevalence of the use of the internet and Internet-related tools has consistently increased year on year (Roser et al., 2020 ). Even though the social media use is widespread and facilitates communication when it is used normally, it can negatively affect daily life when it is used excessively by some individuals. Literature reviews have shown that social media addiction has been mostly studied in East Asian countries like China, Japan, and South Korea (e.g., Bian & Leung, 2015 ; Kwon et al., 2013 ; Tateno et al., 2019 ). In this respect, when the prevalence of social media use among Turkish people and the different cultural context of the present study are considered, the findings would arguably make important contributions to the current literature. Furthermore, the present study appears to be the first to examine the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

Older aged adolescents and emerging adults are inextricably connected with technology in terms of their social media use and stand out as an important risk group in relation to problematic social media use (Griffiths et al., 2014 ). Many young adults closely follow technological developments and often adopt every innovation that arises into their lives without wasting time (Kuyucu, 2017 ). When such use becomes problematic, some individuals experience serious difficulty in maintaining their mental health. For example, cross-sectional studies among adolescents (Woods & Scott, 2016 ) and young adults (Larcombe et al., 2016 ) have found that social media addiction can lead to stress, anxiety, and depression. Moreover, the establishment of close relationships as a young adult is an important stage of emotional and social development (Cashen & Grotevant, 2019 ; Orenstein, & Lewis, 2020 ). Romantic relationship satisfaction may be seen as an important indicator of young people’s ability to engage in intimacy in a healthy manner (Orenstein & Lewis, 2020 ). Therefore, the findings obtained as a result of examining the relationships between social media addiction, psychological distress, and romantic relationship satisfaction among young people will contribute to an understanding of the associations between the psychological and social variables regarding maintenance of their mental health and their success in establishing close relationships.

In previous studies of the variables examined in the present study, even though studies examining the three variables dichotomously have been conducted (e.g., Abbasi, 2019a , b ; Bodenmann et al., 2007 ; Keles et al., 2020 ; Larcombe et al., 2016 ; Whisman, 1999 ), no research examining social media addiction, psychological distress (depression, anxiety and stress), and romantic relationship satisfaction together has been published. In particular, there is no study examining the role of psychological distress mediating between social media addiction and relationship satisfaction. In this respect, the results of the present study may also allow the findings of previous studies (which have been conducted with the aim of identifying the relationship between these variables) to be evaluated from a wider perspective.

Consequently, given the aforementioned theoretical explanations and the research findings, it has been demonstrated that social media addiction appears to induce both psychological distress and a low level of romantic relationship satisfaction (e.g., Demircioğlu & Köse, 2018 ; Woods & Scott, 2016 ). This is due to the deterioration of individuals’ mental health that can arise as a result of social media addiction (Baker & Algorta, 2016 ; Dhir et al., 2018 ), and in contrast to the advantages of developing relationships, it can lead to romantic relationship dissatisfaction (Abbasi, 2019b ; Muise et al., 2009 ). Therefore, when the relationships between social media addiction, psychological distress, and romantic relationship satisfaction are evaluated simultaneously, psychological distress may represent a mediating variable between social media addiction and romantic relationship satisfaction. Consequently, it was hypothesized that psychological distress would mediate the association between social media addiction and relationship satisfaction.

Participants and Procedure

The present cross-sectional study was carried out on a convenience sample of university students from three universities that are located in the west, middle, and east part of Turkey. A total of 350 surveys were originally distributed. Of these, 16 participants were removed because of incomplete data, yielding a final sample of 334 participants aged between 18 and 29 years ( M  = 20.71 years, SD  = 2.18). The participants comprised 214 females (64%) and 120 males (36%), of which 90 were freshmen, 87 were sophomores, 84 were junior students, and 73 were senior students. Participants reported that they were currently in a romantic relationship and reported having an average of 3.21 romantic relationships to date ( SD  = 2.21). Table 1 shows the detailed demographic characteristics of the participants. Written informed consent was obtained from the volunteer participants prior to participation in the study. Research participants were assured of the confidentiality of the collected data. Data collection was carried out through a “paper-and-pencil” survey in the classroom environment. The surveys took less than 15 min to complete.

Relationship Assessment Scale (RAS)

The RAS was designed to assess general relationship satisfaction (Hendrick, 1988 ). Items (e.g., “In general, how satisfied are you with your relationship?”) utilize a seven-point Likert scale ranging from 1 ( low ) to 7 ( high ). The total score ranges from 7 to 49. The higher the score, the higher the relationship satisfaction. Hendrick ( 1988 ) reported very good reliability. The RAS was adapted into Turkish by Curun ( 2001 ) with very good internal consistency. In the present study, the internal consistency of this scale was also good ( α  = 0.80).

Social Media Disorder Scale (SMD)

The SMD was designed to assess overall social media addiction, and the items were developed by adapting the DSM-5 criteria for Internet gaming disorder (van den Eijnden et al., 2016 ). This scale includes nine items (e.g., “… regularly found that you can't think of anything else but the moment that you will be able to use social media again?”) to which participants indicate their level of agreement on a five-point Likert scale ranging from 0 ( never ) to 4 ( always ). The total score ranges from 0 to 36. The higher the score, the higher the risk of social media addiction. The SMD was adapted to Turkish by Savci et al. ( 2018 ) and has very good internal consistency. In the present study, the internal consistency of this scale was also very good ( α  = 0.88).

Depression Anxiety and Stress Scale (DASS-21)

The DASS was designed to assess the level of psychological distress (Henry & Crawford, 2005 ). The scale consists of 21 items that are rated on a four-point Likert scale from 0 ( did not apply to me at all ) to 3 ( applied to me very much or most of the time ) and comprises three sub-scales: depression (seven items; e.g., “I found it difficult to work up the initiative to do things”), anxiety (seven items; e.g., “I felt I was close to panic”), and stress (seven items; “I found myself getting agitated”). The scores range from 0 to 21 for each sub-scale. The DASS-21 subscales’ scores were multiplied by two based on Lovibund and Lovibond’s ( 1995 ) suggestion to the cut-offs (see Appendix 1 ). The DASS-21 was adapted to Turkish by Yilmaz et al. ( 2017 ) with good to very good internal consistencies. In the present study, the internal consistency of the sub-scales were all very good ( α  = 0.89, 0.82, 0.85, respectively).

Statistical Analyses

Pearson correlations, means, and standard deviations were examined as preliminary analyses for all study variables. To examine whether the association between social media addiction and relationship satisfaction was mediated by psychological distress, the mediation model was calculated using the PROCESS macro (model 4), developed by Hayes ( 2018 ). As recommended by Hayes ( 2018 ), all regression/path coefficients are in unstandardized form. A total of 10,000 bootstrap samples were generated and bias corrected 95% confidence intervals calculated.

Written informed consent was obtained from the volunteer participants prior to participation in the study. This research was approved by Artvin Coruh University Scientific Research and Ethical Review Board (REF: E.5375).

Descriptive Statistics

Bivariate Pearson correlations among study variables were investigated (see Table 2 ). As expected, social media addiction was significantly and positively correlated with depression, anxiety, and stress. There was a significant negative correlation between social media addiction and relationship satisfaction.

Results indicated that 156 participants had no depressive symptoms (46.7%), 54 participants had mild depressive symptoms (16.2%), and the remainder had depressive symptoms (16.5% moderate, 9.9% severe, and 10.8% extremely severe). Moreover, 101 participants had no anxiety symptoms (30.2%), 30 participants had mild anxiety symptoms (9.0%), and the remainder had anxiety symptoms (20.4% moderate, 15.6% severe, and 24.9% extremely severe). Finally, 163 participants had no stress symptoms (48.8%), 47 participants had mild depressive symptoms (14.1%), and the remainder had stress symptoms (17.7% moderate, 12.6% severe, and 6.9% extremely severe) (see Appendix 1 ).

Statistical Assumption Tests

Prior to mediation analysis, statistical assumptions were evaluated. Skewness and kurtosis values (> ± 2; George & Mallery, 2003 ) were checked for normality, and there were no violations (see Table 3 ). All reliability coefficients were above Nunnally and Bernstein’s ( 1994 ) 0.70 criterion. Multicollinearity was checked with variance inflated factor (VIF), tolerance, and Durbin-Watson (DW) value. The results showed that VIF ranged from 1.47 to 2.09 and tolerance ranged from 0.48 to 0.87. These findings also showed that there was no multiple linearity problem according to Field’s ( 2013 ) recommendation. Also, the DW value was 1.82 indicating no significant correlations between the residuals.

Mediation Analyses

Applying PROCESS model 4, the analysis assessed whether psychological distress mediated the relationship between social media addiction and relationship satisfaction (see Table 4 ; Fig.  1 ). The results showed a significant total direct effect ( path c ; without mediator) of social media addiction on relationship satisfaction (B =  − 0.36, t (334)  =  − 4.74, p  = 0.001, 95% CI =  − 0.51, − 0.21), significant direct effect ( path c ; with mediator) (B =  − 0.16, t (334)  =  − 2.11, p  = 0.03, 95% CI =  − 0.04, − 0.01), and a significant indirect effect via psychological distress (total B =  − 0.20, 95% CI =  − 0.29, − 0.12).

The mediation model. * p  < .05. ** p  < .001

The results also showed that the social media addiction was associated with higher depression scores (path a 1 ; B = 0.23, p  = 0.001), anxiety scores (path a 2 ; B = 0.23, p  = 0.001), and stress scores (path a 3 ; B = 0.27, p  = 0.001), and these, in turn, were negatively associated with relationship satisfaction (path b 1, b 2, b 3 ; B =  − 0.28, B =  − 0.28, B =  − 0.26, all p values < 0.05, respectively).

In contemporary society, rapidly developing technology has entered human life, but some individuals may have difficulty in adapting to the innovations brought by such technology. Consequently, some individuals may experience psychological and social problems. Social media use, which has markedly increased in the past decade, can cause psychological distress (e.g., Keles et al., 2020 ; Marino et al., 2018 ) and the deterioration of interpersonal relationships (e.g., Glaser et al., 2018 ; Kuss & Griffiths, 2017 ; Young, 2019 ) among a minority of individuals. In this context, the main purpose of the present study was to evaluate the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction.

According to the findings, a high level of social media addiction leads to a decrease in relationship satisfaction. Consequently, the first hypothesis was confirmed. A recent study conducted by Abbasi ( 2019b ) found that social media addiction was negatively associated with romantic relationship commitment. In another recent study, it was emphasized that social media addiction results in deception between couples through social media and may lead to the deterioration of relationships as a consequence (Abbasi, 2019a ). In addition, social media addiction not only leads to physical and emotional deception but also appears to negatively impact on the quality of romantic relationships (Demircioğlu & Köse 2018 ; Valenzuela et al., 2014 ). Therefore, the findings obtained in the present study are in line with the findings of previous research.

In the study here, the findings showed that a high level of social media addiction appears to result in psychological distress. Dhir et al. ( 2018 ) argued that social media addiction triggers social media fatigue, leading to anxiety and depression. Similarly, social media addiction has been found to be associated with depression, anxiety (Woods & Scott, 2016 ) and stress (Larcombe et al., 2016 ). In addition, a recent meta-analysis also concluded that social media addiction is closely and positively associated depression, anxiety and stress (Marino et al., 2018 ). Therefore, the findings of the present study are consistent with previous research.

Thirdly, the findings indicate that individuals who experience psychological distress have a low level of satisfaction in their romantic relationships. Whisman ( 1999 ) found that psychological distress positively predicted relationship dissatisfaction. It has also been suggested that couples with high levels of stress experience dissatisfaction in their romantic relationships (Bodenmann et al., 2007 ). In addition, there have also been a number of studies which indicate that the relationship satisfaction of individuals with high levels of depression is low (Cramer, 2004a , b ; Tolpin et al., 2006 ). In this respect, the findings obtained from the present study are similar to the findings of the previous studies.

Within the scope of this study, it was hypothesized that psychological distress would mediate between social media addiction and relationship satisfaction. In this sense, the study showed that social media addiction predicted romantic relationship satisfaction, partially mediated by psychological distress. Consequently, the fourth hypothesis of the research was also confirmed. No previous studies have examined the effect of psychological distress in the relationship between social media addiction and relationship satisfaction. However, there are research findings which provide evidence that social media addiction predicts both psychological distress (e.g., Larcombe et al., 2016 ; Woods & Scott, 2016 ) and relationship dissatisfaction (e.g., Demircioğlu & Köse, 2018 ; Valenzuela et al., 2014 ) and that psychological distress predicts relationship dissatisfaction (e.g., Bodenmann et al., 2007 ; Whisman, 1999 ). Due to the consideration of a variable’s mediating conditions (Barron & Kenny, 1986 ), it may be asserted that the findings of the previous studies in the literature and the findings of this research are consistent. Furthermore, it has been demonstrated that technological addiction, such as Internet addiction and smartphone addiction, is associated with psychological distress (McNicol & Thorsteinsson, 2017 ; Samaha & Hawi, 2016 ; Young & Rogers, 1998 ). Psychological distress may also predict variables such as closeness in relationships (Manne et al., 2010 ), dating violence (Cascardi, 2016 ), and social support (Robitaille et al., 2012 ) which are based on interpersonal relationships. It is therefore suggested that there is similarity between these findings and the findings of the present study. Consequently, it may be that the results of the studies conducted previously support the findings of this the present research indirectly, if not directly.

In the study here, the mediating role of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was investigated. However, there could be some other variables that can mediate the relationship between social media addiction and romantic relationship satisfaction. For instance, romantic relationships are considered interpersonal (Knap et al., 2002 ); therefore, it can be assumed that interpersonal relationships and communication skills can be seen as potential mediators of the relationship between social media addiction and romantic relationship satisfaction. Additionally, given that psychological problems are the indicators of poor mental health (American Psychiatric Association, 2013 ), it can be assumed that variables (i.e., other indicators of poor mental health such as burnout, somatization, and hostility) would mediate the relationship between social media addiction and romantic relationship satisfaction. Therefore, future studies should investigate such relationships more closely.

When the role of social media addiction in the development of psychological distress is considered, it is necessary for social media addiction to be included in the process of forming the content of the intervention programs that aim to treat psychological distress. As such, it is interesting that an intervention program aimed at decreasing the level of social media addiction was also found to have a beneficial impact on individuals’ mental health (Hou et al., 2019 ). Likewise, the treatment of couples’ social media usage habits in family and couple therapies may be effective in terms of the efficacy of the therapy, since social media addiction decreases satisfaction in romantic relationships. Moreover, given the mediation relationships in the present research, the results may provide a more holistic viewpoint for mental health professionals which consider all of the three variables (social media addiction, psychological distress, and romantic relationship satisfaction) rather than a focus on only one. In this context, the following suggestions are made: to prevent social media addiction, effective Internet use skills can be taught to couples. In addition, awareness-raising skills such as yoga and meditation could be provided to individuals to protect them from social media addiction and psychological distress.

In terms of the study’s participatory group, it is significant that social media addiction (Kittinger et al., 2012 ; Koc & Gulyagci, 2013 ), psychological distress (Canby et al., 2015 ; Larcombe et al., 2016 ), and relationship satisfaction problems (Bruner et al., 2015 ; Roberts & David, 2016 ) are frequently experienced by university students. Consequently, the findings of the present study may be of particular help to specialists who work in the psychological counseling centers of universities. Within this framework, meetings, conferences, and psycho-educational group activities could be carried out to improve relationship building skills, as well as activities preventing social media addiction and psychological distress.

The present study has some limitations. Firstly, the data comprised self-report scales, which may decrease internal reliability, a limitation which may be prevented through the use of different methods of data collection. Secondly, the generalizability of the findings is limited since the sample was based on convenience sampling. Thirdly, the research design was cross-sectional. This may make it difficult to explain the cause-effect relationship of variables in the study, and therefore, experimental and longitudinal studies are recommended in future research which should examine the relationship between these variables. Finally, only the mediating role of psychological distress was examined in the research. Other possible mediating variables were not examined.

In the present research, the mediation of psychological distress in the relationship between social media addiction and romantic relationship satisfaction was empirically tested. Results showed that social media addiction predicted the partial mediation of depression, anxiety, and stress on romantic relationship satisfaction. In other words, social media addiction apparently increased individuals’ depression, anxiety, and stress levels, and this situation decreased the level of satisfaction in individual’s romantic relationships. In the present study, psychological and social variables were examined simultaneously. Overall, this study suggests that social media addiction may have a meaningful but negative impact on romantic relationship satisfaction via depression, anxiety, and stress.

Abbasi, I. S. (2019a). Social media addiction in romantic relationships: Does user’s age influence vulnerability to social media infidelity? Personality and Individual Differences, 139 , 277–280. https://doi.org/10.1016/j.paid.2018.10.038

Article   Google Scholar  

Abbasi, I. S. (2019b). Social media and committed relationships: What factors make our romantic relationship vulnerable? Social Science Computer Review, 37 (3), 425–435. https://doi.org/10.1177/0894439318770609

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). Author. https://doi.org/10.1176/appi.books.9780890425596

Book   Google Scholar  

Andreassen, C. S., Torsheim, T., Brunborg, G. S., & Pallesen, S. (2012). Development of a facebook addiction scale. Psychological Reports, 110 (2), 501–517. https://doi.org/10.2466/02.09.18.PR0.110.2.501-517

Article   PubMed   Google Scholar  

Baker, D. A., & Algorta, G. P. (2016). The relationship between online social networking and depression: A systematic review of quantitative studies. CyberPsychology, Behavior, and Social Networking, 19 (11), 638–648. https://doi.org/10.1089/cyber.2016.0206

Barron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51 (6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Bian, M., & Leung, L. (2015). Linking loneliness, shyness, smartphone addiction symptoms, and patterns of smartphone use to social capital. Social Science Computer Review, 33 (1), 61–79. https://doi.org/10.1177/0894439314528779

Bodenmann, G., Ledermann, T., & Bradbury, T. N. (2007). Stress, sex, and satisfaction in marriage. Personal Relationships, 14 (4), 551–569. https://doi.org/10.1111/j.1475-6811.2007.00171.x

Bruner, M. R., Kuryluk, A. D., & Whitton, S. W. (2015). Attention-deficit/hyperactivity disorder symptom levels and romantic relationship quality in college students. Journal of American College Health, 63 (2), 98–108. https://doi.org/10.1080/07448481.2014.975717

Canby, N. K., Cameron, I. M., Calhoun, A. T., & Buchanan, G. M. (2015). A brief mindfulness intervention for healthy college students and its effects on psychological distress, self-control, meta-mood, and subjective vitality. Mindfulness, 6 (5), 1071–1081. https://doi.org/10.1007/s12671-014-0356-5

Cascardi, M. (2016). From violence in the home to physical dating violence victimization: The mediating role of psychological distress in a prospective study of female adolescents. Journal of Youth and Adolescence, 45 (4), 777–792. https://doi.org/10.1007/s10964-016-0434-1

Cashen, K. K., & Grotevant, H. D. (2019). Relational competence in emerging adult adoptees: Conceptualizing competence in close relationships.  Journal of Adult Development . Advanced online publication. https://doi.org/10.1007/s10804-019-09328-x

Cramer, D. (2004a). Emotional support, conflict, depression, and relationship satisfaction in a romantic partner. Journal of Psychology, 138 (6), 532–542. https://doi.org/10.3200/JRLP.138.6.532-542

Cramer, D. (2004b). Satisfaction with a romantic relationship, depression, support and conflict. Psychology and Psychotherapy: Theory, Research and Practice, 77 (4), 449–461. https://doi.org/10.1348/1476083042555389

Curun, F. (2001). The effects of sexism and sex role orientation on relationship satisfaction . [Unpublished Master's thesis, Middle East Technical University], Ankara, Turkey.

Demircioğlu, Z. I., & Köse, A. G. (2018). Effects of attachment styles, dark triad, rejection sensitivity, and relationship satisfaction on social media addiction: A mediated model.  Current Psychology . Advanced online publication. https://doi.org/10.1007/s12144-018-9956-x

Dhir, A., Yossatorn, Y., Kaur, P., & Chen, S. (2018). Online social media fatigue and psychological wellbeing – A study of compulsive use, fear of missing out, fatigue, anxiety and depression. International Journal of Information Management, 40 , 141–152. https://doi.org/10.1016/j.ijinfomgt.2018.01.012

Field, A. (2013). Discovering statistics using IBM SPSS statistics (4th ed.). Sage.

Google Scholar  

Fincham, F. D., Rogge, R., & Beach, S. R. H. (2018). Relationship satisfaction. In A. Vangelisti & D. Perlman (Eds.), The Cambridge handbook of personal relationships (pp. 579–594). Cambridge University Press. https://doi.org/10.1017/CBO9780511606632.032

Chapter   Google Scholar  

Fuchs, C. (2017). Social media: A critical introduction . Sage.

George, D., & Mallery, P. (2003). SPSS for windows step by step: A simple guide and reference . Allyn and Bacon.

Glaser, P., Liu, J. H., Hakim, M. A., Vilar, R., & Zhang, R. (2018). Is social media use for networking positive or negative? Offline social capital and internet addiction as mediators for the relationship between social media use and mental health. New Zealand Journal of Psychology, 47 (3), 12–18.

Griffiths, M. D. (2012). Facebook addiction: Concerns, criticism, and recommendations: A response to Andreassen and colleagues. Psychological Reports, 110 , 518–520. https://doi.org/10.2466/01.07.18.PR0.110.2.518-520

Griffiths, M. D. (2013). Social networking addiction: Emerging themes and issues. Journal of Addiction Research & Therapy, 4 (5), e118. https://doi.org/10.4172/2155-6105.1000e118

Griffiths, M. D., Kuss, D. J., & Demetrovics, Z. (2014). Social networking addiction: An overview of preliminary findings. In K. Rosenberg & L. Feder (Eds.), Behavioral Addictions: Criteria, Evidence and Treatment (pp. 119–141). Elsevier.

Hayes, A. F. (2018). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach . Guilford Publications.

Hazar, M. (2011). Sosyal medya bağımlılığı: Bir alan çalışması. İletişim, Kuram Ve Araştırma Dergisi, 32 , 151–175.

Heaney, C. A., & Israel, B. A. (2008). Social networks and social support. In K. Glanz, B. K. Rimer, & K. Viswanath (Eds.), Health behavior and health education: Theory, research, and practice (pp. 189–210). Wiley.

Hendrick, S. S. (1988). A generic measure of relationship satisfaction. Journal of Marriage and the Family, 50 , 93–98. https://doi.org/10.2307/352430

Henry, J. D., & Crawford, J. R. (2005). The short-form version of the depression anxiety stress scales (DASS-21): Construct validity and normative data in a large non-clinical sample. British Journal of Clinical Psychology, 44 (2), 227–239. https://doi.org/10.1348/014466505X29657

Hou, Y., Xiong, D., Jiang, T., Song, L., & Wang, Q. (2019). Social media addiction: Its impact, mediation, and intervention. Cyberpsychology: Journal of Psychosocial Research on Cyberspace, 13 (1), 4. https://doi.org/10.5817/CP2019-1-4

Keles, B., McCrae, N., & Grealish, A. (2020). A systematic review: The influence of social media on depression, anxiety and psychological distress in adolescents. International Journal of Adolescence and Youth, 25 (1), 79–93. https://doi.org/10.1080/02673843.2019.1590851

Kittinger, R., Correia, C. J., & Irons, J. G. (2012). Relationship between Facebook use and problematic internet use among college students. Cyberpsychology, Behavior, and Social Networking, 15 (6), 324–327. https://doi.org/10.1089/cyber.2010.0410

Knap, M. L., Daly, J. A., Albada, K. F., & Miller, G. R. (2002). Handbook of interpersonal communication . Sage.

Koc, M., & Gulyagci, S. (2013). Facebook addiction among Turkish college students: The role of psychological health, demographic, and usage characteristics. Cyberpsychology, Behavior, and Social Networking, 16 (4), 279–284. https://doi.org/10.1089/cyber.2012.0249

Kuss, D., & Griffiths, M. (2017). Social networking sites and addiction: Ten lessons learned. International Journal of Environmental Research and Public Health, 14 (3), 311. https://doi.org/10.3390/ijerph14030311

Article   PubMed   PubMed Central   Google Scholar  

Kuyucu, M. (2017). Y kuşağı ve teknoloji: Y kuşağının iletişim teknolojilerini kullanım alışkanlıkları. Gümüşhane Üniversitesi İletişim Fakültesi Elektronik Dergisi, 5 (2), 845–872. https://doi.org/10.19145/e-gifder.285714

Kwon, M., Kim, D. J., Cho, H., & Yang, S. (2013). The smartphone addiction scale: Development and validation of a short version for adolescents. PLoS ONE, 8 (12), e83558. https://doi.org/10.1371/journal.pone.0083558

Article   CAS   PubMed   PubMed Central   Google Scholar  

Larcombe, W., Finch, S., Sore, R., Murray, C. M., Kentish, S., Mulder, R. A., … & Williams, D. A. (2016). Prevalence and socio-demographic correlates of psychological distress among students at an Australian university.  Studies in Higher Education ,  41 (6), 1074-1091. https://doi.org/10.1080/03075079.2014.966072

Lovibund, S., & Lovibund, P. (1995). Manual for the depression anxiety stress scales . Psychology Foundation.

Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015). The effects of technostress and switching stress on discontinued use of social networking services: A study of Facebook use. Information Systems Journal, 25 (3), 275–308. https://doi.org/10.1111/isj.12068

Manne, S., Badr, H., Zaider, T., Nelson, C., & Kissane, D. (2010). Cancer-related communication, relationship intimacy, and psychological distress among couples coping with localized prostate cancer. Journal of Cancer Survivorship, 4 (1), 74–85. https://doi.org/10.1007/s11764-009-0109-y

Marino, C., Gini, G., Vieno, A., & Spada, M. M. (2018). The associations between problematic Facebook use, psychological distress and well-being among adolescents and young adults: A systematic review and meta-analysis. Journal of Affective Disorders, 226 , 274–281. https://doi.org/10.1016/j.jad.2017.10.007

McNicol, M. L., & Thorsteinsson, E. B. (2017). Internet addiction, psychological distress, and coping responses among adolescents and adults. Cyberpsychology, Behavior, and Social Networking, 20 (5), 296–304. https://doi.org/10.1089/cyber.2016.0669

Meena, P. S., Soni, R., Jain, M., & Paliwal, S. (2015). Social networking sites addiction and associated psychological problems among young adults: A study from North India. Sri Lanka Journal of Psychiatry, 6 (1), 14–16. https://doi.org/10.4038/sljpsyc.v6i1.8055

Muise, A., Christofides, E., & Desmarais, S. (2009). More information than you ever wanted: Does Facebook bring out the green-eyed monster of jealousy? CyberPsychology & Behavior, 12 (4), 441–444. https://doi.org/10.1089/cpb.2008.0263

Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.

Orenstein, G. A., & Lewis, L. (2020). Eriksons stages of psychosocial development. In  StatPearls [Internet] . StatPearls Publishing. Retrived May 12, 2020, from https://www.ncbi.nlm.nih.gov/books/NBK556096/

Pollet, T. V., Roberts, S. G., & Dunbar, R. I. (2011). Use of social network sites and instant messaging does not lead to increased offline social network size, or to emotionally closer relationships with offline network members. Cyberpsychology, Behavior, and Social Networking, 14 (4), 253–258. https://doi.org/10.1089/cyber.2010.0161

Roberts, J. A., & David, M. E. (2016). My life has become a major distraction from my cell phone: Partner phubbing and relationship satisfaction among romantic partners. Computers in Human Behavior, 54 , 134–141. https://doi.org/10.1016/j.chb.2015.07.058

Robitaille, A., Orpana, H., & McIntosh, C. N. (2012). Reciprocal relationship between social support and psychological distress among a national sample of older adults: An autoregressive cross-lagged model. Canadian Journal on Aging/la Revue Canadienne Du Vieillissement, 31 (1), 13–24. https://doi.org/10.1017/S0714980811000560

Roser, M., Ritchie, H., & Ortiz-Ospina, E. (2020). Internet. Retrieved May 12, 2020, from https://ourworldindata.org/internet

Samaha, M., & Hawi, N. S. (2016). Relationships among smartphone addiction, stress, academic performance, and satisfaction with life. Computers in Human Behavior, 57 , 321–325. https://doi.org/10.1016/j.chb.2015.12.045

Savci, M., Ercengiz, M., & Aysan, F. (2018). Turkish adaptation of the social media disorder scale in adolescents. Archives of Neuropsychiatry, 55 (3), 248–255. https://doi.org/10.29399/npa.19285

Tateno, M., Kim, D. J., Teo, A. R., Skokauskas, N., Guerrero, A. P., & Kato, T. A. (2019). Smartphone addiction in Japanese college students: usefulness of the Japanese version of the smartphone addiction scale as a screening tool for a new form of internet addiction. Psychiatry Investigation, 16 (2), 115–120. https://doi.org/10.30773/pi.2018.12.25.2

Tolpin, L. H., Cohen, L. H., Gunthert, K. C., & Farrehi, A. (2006). Unique effects of depressive symptoms and relationship satisfaction on exposure and reactivity to daily romantic relationship stress. Journal of Social and Clinical Psychology, 25 (5), 565–583. https://doi.org/10.1521/jscp.2006.25.5.565

Tutgun-Ünal, A., & Deniz, L. (2015). Development of the social media addiction scale. Online Academic Journal of Information Technology, 6 (21), 51–70. https://doi.org/10.5824/1309-1581.2015.4.004.x

Valentini, C. (2015). Is using social media “good” for the public relations profession? A Critical Reflection. Public Relations Review, 41 (2), 170–177. https://doi.org/10.1016/j.pubrev.2014.11.009

Valenzuela, S., Halpern, D., & Katz, J. E. (2014). Social network sites, marriage well-being and divorce: Survey and state-level evidence from the United States. Computers in Human Behavior, 36, 94–101. https://doi.org/10.1016/j.chb.2014.03.034

van den Eijnden, R. J., Lemmens, J. S., & Valkenburg, P. M. (2016). The social media disorder scale. Computers in Human Behavior, 61 , 478–487. https://doi.org/10.1016/j.chb.2016.03.038

Whisman, M. A. (1999). Marital dissatisfaction and psychiatric disorders: Results from the national comorbidity survey. Journal of Abnormal Psychology, 108 (4), 701–706. https://doi.org/10.1037/0021-843X.108.4.701

Article   CAS   PubMed   Google Scholar  

Willard, F. H., Jr. (2011). His needs, her needs: Building an affair-proof marriage . Revell.

Woods, H. C., & Scott, H. (2016). # Sleepyteens: Social media use in adolescence is associated with poor sleep quality, anxiety, depression and low self-esteem. Journal of Adolescence, 51 , 41–49. https://doi.org/10.1016/j.adolescence.2016.05.008

Yen, J. Y., Ko, C. H., Yen, C. F., Chen, S. H., Chung, W. L., & Chen, C. C. (2008). Psychiatric symptoms in adolescents with Internet addiction: Comparison with substance use. Psychiatry and Clinical Neurosciences, 62 (1), 9–16. https://doi.org/10.1111/j.1440-1819.2007.01770.x

Yılmaz, Ö., Boz, H., & Arslan, A. (2017). Depresyon anksiyete stres ölçeğinin (DASS 21) Türkçe kısa formunun geçerlilik-güvenilirlik çalışması. Finans Ekonomi Ve Sosyal Araştırmalar Dergisi, 2 (2), 78–91.

Young, K. S., & Rogers, R. C. (1998). The relationship between depression and Internet addiction. CyberPsychology & Behavior, 1 (1), 25–28. https://doi.org/10.1089/cpb.1998.1.25

Young, G. K. (2019).  Examination of communication and social media usage among socially anxious individuals . [Doctoral dissertation, University of Mississippi]. USA.

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Satici, B., Kayis, A.R. & Griffiths, M.D. Exploring the Association Between Social Media Addiction and Relationship Satisfaction: Psychological Distress as a Mediator. Int J Ment Health Addiction 21 , 2037–2051 (2023). https://doi.org/10.1007/s11469-021-00658-0

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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.

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• BOOK REVIEW
• 29 March 2024

The great rewiring: is social media really behind an epidemic of teenage mental illness?

• Candice L. Odgers 0

Candice L. Odgers is the associate dean for research and a professor of psychological science and informatics at the University of California, Irvine. She also co-leads international networks on child development for both the Canadian Institute for Advanced Research in Toronto and the Jacobs Foundation based in Zurich, Switzerland.

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Social-media platforms aren’t always social. Credit: Getty

The Anxious Generation: How the Great Rewiring of Childhood is Causing an Epidemic of Mental Illness Jonathan Haidt Allen Lane (2024)

Two things need to be said after reading The Anxious Generation . First, this book is going to sell a lot of copies, because Jonathan Haidt is telling a scary story about children’s development that many parents are primed to believe. Second, the book’s repeated suggestion that digital technologies are rewiring our children’s brains and causing an epidemic of mental illness is not supported by science. Worse, the bold proposal that social media is to blame might distract us from effectively responding to the real causes of the current mental-health crisis in young people.

Haidt asserts that the great rewiring of children’s brains has taken place by “designing a firehose of addictive content that entered through kids’ eyes and ears”. And that “by displacing physical play and in-person socializing, these companies have rewired childhood and changed human development on an almost unimaginable scale”. Such serious claims require serious evidence.

Collection: Promoting youth mental health

Haidt supplies graphs throughout the book showing that digital-technology use and adolescent mental-health problems are rising together. On the first day of the graduate statistics class I teach, I draw similar lines on a board that seem to connect two disparate phenomena, and ask the students what they think is happening. Within minutes, the students usually begin telling elaborate stories about how the two phenomena are related, even describing how one could cause the other. The plots presented throughout this book will be useful in teaching my students the fundamentals of causal inference, and how to avoid making up stories by simply looking at trend lines.

Hundreds of researchers, myself included, have searched for the kind of large effects suggested by Haidt. Our efforts have produced a mix of no, small and mixed associations. Most data are correlative. When associations over time are found, they suggest not that social-media use predicts or causes depression, but that young people who already have mental-health problems use such platforms more often or in different ways from their healthy peers 1 .

These are not just our data or my opinion. Several meta-analyses and systematic reviews converge on the same message 2 – 5 . An analysis done in 72 countries shows no consistent or measurable associations between well-being and the roll-out of social media globally 6 . Moreover, findings from the Adolescent Brain Cognitive Development study, the largest long-term study of adolescent brain development in the United States, has found no evidence of drastic changes associated with digital-technology use 7 . Haidt, a social psychologist at New York University, is a gifted storyteller, but his tale is currently one searching for evidence.

Of course, our current understanding is incomplete, and more research is always needed. As a psychologist who has studied children’s and adolescents’ mental health for the past 20 years and tracked their well-being and digital-technology use, I appreciate the frustration and desire for simple answers. As a parent of adolescents, I would also like to identify a simple source for the sadness and pain that this generation is reporting.

A complex problem

There are, unfortunately, no simple answers. The onset and development of mental disorders, such as anxiety and depression, are driven by a complex set of genetic and environmental factors. Suicide rates among people in most age groups have been increasing steadily for the past 20 years in the United States. Researchers cite access to guns, exposure to violence, structural discrimination and racism, sexism and sexual abuse, the opioid epidemic, economic hardship and social isolation as leading contributors 8 .

How social media affects teen mental health: a missing link

The current generation of adolescents was raised in the aftermath of the great recession of 2008. Haidt suggests that the resulting deprivation cannot be a factor, because unemployment has gone down. But analyses of the differential impacts of economic shocks have shown that families in the bottom 20% of the income distribution continue to experience harm 9 . In the United States, close to one in six children live below the poverty line while also growing up at the time of an opioid crisis, school shootings and increasing unrest because of racial and sexual discrimination and violence.

The good news is that more young people are talking openly about their symptoms and mental-health struggles than ever before. The bad news is that insufficient services are available to address their needs. In the United States, there is, on average, one school psychologist for every 1,119 students 10 .

Haidt’s work on emotion, culture and morality has been influential; and, in fairness, he admits that he is no specialist in clinical psychology, child development or media studies. In previous books, he has used the analogy of an elephant and its rider to argue how our gut reactions (the elephant) can drag along our rational minds (the rider). Subsequent research has shown how easy it is to pick out evidence to support our initial gut reactions to an issue. That we should question assumptions that we think are true carefully is a lesson from Haidt’s own work. Everyone used to ‘know’ that the world was flat. The falsification of previous assumptions by testing them against data can prevent us from being the rider dragged along by the elephant.

A generation in crisis

Two things can be independently true about social media. First, that there is no evidence that using these platforms is rewiring children’s brains or driving an epidemic of mental illness. Second, that considerable reforms to these platforms are required, given how much time young people spend on them. Many of Haidt’s solutions for parents, adolescents, educators and big technology firms are reasonable, including stricter content-moderation policies and requiring companies to take user age into account when designing platforms and algorithms. Others, such as age-based restrictions and bans on mobile devices, are unlikely to be effective in practice — or worse, could backfire given what we know about adolescent behaviour.

A third truth is that we have a generation in crisis and in desperate need of the best of what science and evidence-based solutions can offer. Unfortunately, our time is being spent telling stories that are unsupported by research and that do little to support young people who need, and deserve, more.

Nature 628 , 29-30 (2024)

doi: https://doi.org/10.1038/d41586-024-00902-2

Heffer, T., Good, M., Daly, O., MacDonell, E. & Willoughby, T. Clin. Psychol. Sci. 7 , 462–470 (2019).

Article   Google Scholar  

Hancock, J., Liu, S. X., Luo, M. & Mieczkowski, H. Preprint at SSRN https://doi.org/10.2139/ssrn.4053961 (2022).

Odgers, C. L. & Jensen, M. R. J. Child Psychol. Psychiatry 61 , 336–348 (2020).

Article   PubMed   Google Scholar  

Orben, A. Soc . Psychiatry Psychiatr. Epidemiol. 55 , 407–414 (2020).

Valkenburg, P. M., Meier, A. & Beyens, I. Curr. Opin. Psychol. 44 , 58–68 (2022).

Vuorre, M. & Przybylski, A. K. R. Sci. Open Sci. 10 , 221451 (2023).

Miller, J., Mills, K. L., Vuorre, M., Orben, A. & Przybylski, A. K. Cortex 169 , 290–308 (2023).

Martínez-Alés, G., Jiang, T., Keyes, K. M. & Gradus, J. L. Annu. Rev. Publ. Health 43 , 99–116 (2022).

Danziger, S. Ann. Am. Acad. Pol. Soc. Sci. 650 , 6–24 (2013).

US Department of Education. State Nonfiscal Public Elementary/Secondary Education Survey 2022–2023 (National Center for Education Statistics, 2024).

Google Scholar  

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Is Social Media Addictive? Here’s What the Science Says.

A major lawsuit against Meta has placed a spotlight on our fraught relationship with online social information.

• Share full article

By Matt Richtel

A group of 41 states and the District of Columbia filed suit on Tuesday against Meta , the parent company of Facebook, Instagram, WhatsApp and Messenger, contending that the company knowingly used features on its platforms to cause children to use them compulsively, even as the company said that its social media sites were safe for young people.

“Meta has harnessed powerful and unprecedented technologies to entice, engage and ultimately ensnare youth and teens,” the states said in their lawsuit filed in federal court. “Its motive is profit.”

The accusations in the lawsuit raise a deeper question about behavior: Are young people becoming addicted to social media and the internet? Here’s what the research has found.

What Makes Social Media So Compelling?

Experts who study internet use say that the magnetic allure of social media arises from the way the content plays to our neurological impulses and wiring, such that consumers find it hard to turn away from the incoming stream of information.

David Greenfield, a psychologist and founder of the Center for Internet and Technology Addiction in West Hartford, Conn., said the devices lure users with some powerful tactics. One is “intermittent reinforcement,” which creates the idea that a user could get a reward at any time. But when the reward comes is unpredictable. “Just like a slot machine,” he said. As with a slot machine, users are beckoned with lights and sounds but, even more powerful, information and reward tailored to a user’s interests and tastes.

Adults are susceptible, he noted, but young people are particularly at risk, because the brain regions that are involved in resisting temptation and reward are not nearly as developed in children and teenagers as in adults. “They’re all about impulse and not a lot about the control of that impulse,” Dr. Greenfield said of young consumers.

Moreover, he said, the adolescent brain is especially attuned to social connections, and “social media is all a perfect opportunity to connect with other people.”

Meta responded to the lawsuit by saying that it had taken many steps to support families and teenagers. “We’re disappointed that instead of working productively with companies across the industry to create clear, age-appropriate standards for the many apps teens use, the attorneys general have chosen this path,” the company said in a statement.

Does Compulsion Equal Addiction?

For many years, the scientific community typically defined addiction in relation to substances, such as drugs, and not behaviors, such as gambling or internet use. That has gradually changed. In 2013, the Diagnostic and Statistical Manual of Mental Disorders, the official reference for mental health conditions, introduced the idea of internet gaming addiction but said that more study was warranted before the condition could be formally declared.

A subsequent stud y explored broadening the definition to “internet addiction.” The author suggested further exploring diagnostic criteria and the language, noting, for instance, that terms like “problematic use” and even the word “internet” were open to broad interpretation, given the many forms the information and its delivery can take.

Dr. Michael Rich, the director of the Digital Wellness Lab at Boston Children’s Hospital, said he discouraged the use of the word “addiction” because the internet, if used effectively and with limits, was not merely useful but also essential to everyday life. “I prefer the term ‘Problematic Internet Media Use,” he said, a term that has gained currency in recent years.

Dr. Greenfield agreed that there clearly are valuable uses for the internet and that the definition of how much is too much can vary. But he said there also were clearly cases where excessive use interferes with school, sleep and other vital aspects of a healthy life. Too many young consumers “can’t put it down,” he said. “The internet is a giant hypodermic, and the content, including social media like Meta, are the psychoactive drugs.”

Matt Richtel is a health and science reporter for The Times, based in Boulder, Colo. More about Matt Richtel

A Parent’s Guide to Kids and Social Media

Does your child have an unhealthy relationship with social media? This is what problematic use could look like .

We asked experts for one practical strategy that parents can use with their kids to help mitigate the harms of social media. Here’s what they told us .

There are many tools that allow parents to monitor and set limits on their children’s screen time. Here’s what to know about them .

If you’ve already given your teen full access to social media, these three strategies can help them cut back .

Is social media addictive? Here is what the science says .

A new book argues that banning social media isn’t the answer to online safety. Instead, the author says parents should emphasize the importance of digital literacy and privacy .

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Social Media Addiction and its Effects to Senior High School Students' Behavior in School A Research Study

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Enough (or maybe not so much) has been said about social media and its adverse consequences on humans, particularly the young ones. Yet, the concept is inexhaustible, it remains elusive as we are in a constant flux from one existing social platform to a new and more advanced, sophisticated social platform. Our aim, therefore is to look critically into the adverse effects of excessive use of social media on our growing teens especially in our contemporary era. The question is not whether social media is good or bad but how much good can be derived from it and how much efforts we are putting to curb the bad, negative effects of social media. To begin, what does it mean to be social? What is media? To be social means relating to society or its organization, to interact, mingle and contribute to the betterment of such society or community as the case may be. Media, on the other hand, are the communication outlets or tools used to store and deliver information or data. Thus, when we put these two words together, we have what is called social media. What is worthy of note in this exercise is that social media go beyond the popular Facebook, Twitter, WhatsApp and the likes. What then is social media? What is social media? According to Investopedia, an online resource, "social media is a computer-based technology that facilitates the sharing of ideas, thoughts, and information through the building of virtual networks and communities." By design, social media is Internet-based and gives users quick electronic communication of content. Content includes personal information, documents, videos, and photos. Users engage with social media via a computer, tablet, or smartphone via web-based software or applications.

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Social media's impact on youth is creating additional challenges and opportunities. Social Networking sites provide a platform for discussion on burning issues that has been overlooked in today's scenario. The impact of social networking sites in the changing mind-set of the youth. It was survey type research and data was collected through the questionnaire. 300 sampled youth fill the questionnaire; non-random sampling technique was applied to select sample units. The main objectives were as (1) To analyze the influence of social media on youth social life (2) To assess the beneficial and preferred form of social media for youth (3) To evaluate the attitude of youth towards social media and measure the spending time on social media (4) To recommend some measure for proper use of social media in right direction to inform and educate the people. Collected data was analyzed in term of frequency, percentage, and mean score of statements. Following were main findings Majority of the respondents shows the agreements with these influences of social media. Respondents opine Facebook as their favorite social media form, and then the like Skype as second popular form of social media, the primary place for them, 46 percent responded connect social media in educational institution computer labs, mainstream responded as informative links share, respondents Face main problem during use of social are unwanted messages, social media is beneficial for youth in the field of education, social media deteriorating social norms, social media is affecting negatively on study of youth. Social media promotes unethical pictures, video clips and images among youth, anti-religious post and links create hatred among peoples of different communities, Negative use of social media is deteriorating the relationship among the countries, social media is playing a key role to create political awareness among youth. Introduction Social media is most recent form of media and having many features and characteristics. It have many facilities on same channel like as communicating ,texting, images sharing , audio and video sharing , fast publishing, linking with all over world, direct connecting. it is also cheapest fast access to the world so it is very important for all age of peoples. Its use is increasing day by day with high rate in all over the

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Abstract Utilizing the technology made our life very easier and brought the globe in our hand which has got both pros and cons. Young generation is more of techno oriented than the values that makes them to be depending on the social medias easily that affects the domains of health. A study was conducted to assess the Social media addiction among the paramedical students. Quantitative research approach with non experimental, descriptive research design was used. Non probability convenient sampling technique was used to select 140 para medical students who fulfills the inclusion criteria. Self administered structured questionnaire was used. Modified social media addiction likert scale was used with 20 items. Findings of the study shows that vast majority (103(74%)) of the students were addicted to the social media. To conclude, it is the high time for the policy-makers to restrict on this and make provision to improve the interaction skills. Keywords: Social media addiction, Social interactions.

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Social Media Use and Its Connection to Mental Health: A Systematic Review

Fazida karim.

1 Psychology, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

2 Business & Management, University Sultan Zainal Abidin, Terengganu, MYS

Azeezat A Oyewande

3 Family Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

4 Family Medicine, Lagos State Health Service Commission/Alimosho General Hospital, Lagos, NGA

Lamis F Abdalla

5 Internal Medicine, California Institute of Behavioral Neurosciences and Psychology, Fairfield, USA

Reem Chaudhry Ehsanullah

Safeera khan.

Social media are responsible for aggravating mental health problems. This systematic study summarizes the effects of social network usage on mental health. Fifty papers were shortlisted from google scholar databases, and after the application of various inclusion and exclusion criteria, 16 papers were chosen and all papers were evaluated for quality. Eight papers were cross-sectional studies, three were longitudinal studies, two were qualitative studies, and others were systematic reviews. Findings were classified into two outcomes of mental health: anxiety and depression. Social media activity such as time spent to have a positive effect on the mental health domain. However, due to the cross-sectional design and methodological limitations of sampling, there are considerable differences. The structure of social media influences on mental health needs to be further analyzed through qualitative research and vertical cohort studies.

Introduction and background

Human beings are social creatures that require the companionship of others to make progress in life. Thus, being socially connected with other people can relieve stress, anxiety, and sadness, but lack of social connection can pose serious risks to mental health [ 1 ].

Social media

Social media has recently become part of people's daily activities; many of them spend hours each day on Messenger, Instagram, Facebook, and other popular social media. Thus, many researchers and scholars study the impact of social media and applications on various aspects of people’s lives [ 2 ]. Moreover, the number of social media users worldwide in 2019 is 3.484 billion, up 9% year-on-year [ 3 - 5 ]. A statistic in Figure  1  shows the gender distribution of social media audiences worldwide as of January 2020, sorted by platform. It was found that only 38% of Twitter users were male but 61% were using Snapchat. In contrast, females were more likely to use LinkedIn and Facebook. There is no denying that social media has now become an important part of many people's lives. Social media has many positive and enjoyable benefits, but it can also lead to mental health problems. Previous research found that age did not have an effect but gender did; females were much more likely to experience mental health than males [ 6 , 7 ].

Impact on mental health

Mental health is defined as a state of well-being in which people understand their abilities, solve everyday life problems, work well, and make a significant contribution to the lives of their communities [ 8 ]. There is debated presently going on regarding the benefits and negative impacts of social media on mental health [ 9 , 10 ]. Social networking is a crucial element in protecting our mental health. Both the quantity and quality of social relationships affect mental health, health behavior, physical health, and mortality risk [ 9 ]. The Displaced Behavior Theory may help explain why social media shows a connection with mental health. According to the theory, people who spend more time in sedentary behaviors such as social media use have less time for face-to-face social interaction, both of which have been proven to be protective against mental disorders [ 11 , 12 ]. On the other hand, social theories found how social media use affects mental health by influencing how people view, maintain, and interact with their social network [ 13 ]. A number of studies have been conducted on the impacts of social media, and it has been indicated that the prolonged use of social media platforms such as Facebook may be related to negative signs and symptoms of depression, anxiety, and stress [ 10 - 15 ]. Furthermore, social media can create a lot of pressure to create the stereotype that others want to see and also being as popular as others.

The need for a systematic review

Systematic studies can quantitatively and qualitatively identify, aggregate, and evaluate all accessible data to generate a warm and accurate response to the research questions involved [ 4 ]. In addition, many existing systematic studies related to mental health studies have been conducted worldwide. However, only a limited number of studies are integrated with social media and conducted in the context of social science because the available literature heavily focused on medical science [ 6 ]. Because social media is a relatively new phenomenon, the potential links between their use and mental health have not been widely investigated.

This paper attempt to systematically review all the relevant literature with the aim of filling the gap by examining social media impact on mental health, which is sedentary behavior, which, if in excess, raises the risk of health problems [ 7 , 9 , 12 ]. This study is important because it provides information on the extent of the focus of peer review literature, which can assist the researchers in delivering a prospect with the aim of understanding the future attention related to climate change strategies that require scholarly attention. This study is very useful because it provides information on the extent to which peer review literature can assist researchers in presenting prospects with a view to understanding future concerns related to mental health strategies that require scientific attention. The development of the current systematic review is based on the main research question: how does social media affect mental health?

Research strategy

The research was conducted to identify studies analyzing the role of social media on mental health. Google Scholar was used as our main database to find the relevant articles. Keywords that were used for the search were: (1) “social media”, (2) “mental health”, (3) “social media” AND “mental health”, (4) “social networking” AND “mental health”, and (5) “social networking” OR “social media” AND “mental health” (Table  1 ).

Out of the results in Table  1 , a total of 50 articles relevant to the research question were selected. After applying the inclusion and exclusion criteria, duplicate papers were removed, and, finally, a total of 28 articles were selected for review (Figure  2 ).

PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses

Inclusion and exclusion criteria

Peer-reviewed, full-text research papers from the past five years were included in the review. All selected articles were in English language and any non-peer-reviewed and duplicate papers were excluded from finally selected articles.

Of the 16 selected research papers, there were a research focus on adults, gender, and preadolescents [ 10 - 19 ]. In the design, there were qualitative and quantitative studies [ 15 , 16 ]. There were three systematic reviews and one thematic analysis that explored the better or worse of using social media among adolescents [ 20 - 23 ]. In addition, eight were cross-sectional studies and only three were longitudinal studies [ 24 - 29 ].The meta-analyses included studies published beyond the last five years in this population. Table  2  presents a selection of studies from the review.

IGU, internet gaming disorder; PSMU, problematic social media use

This study has attempted to systematically analyze the existing literature on the effect of social media use on mental health. Although the results of the study were not completely consistent, this review found a general association between social media use and mental health issues. Although there is positive evidence for a link between social media and mental health, the opposite has been reported.

For example, a previous study found no relationship between the amount of time spent on social media and depression or between social media-related activities, such as the number of online friends and the number of “selfies”, and depression [ 29 ]. Similarly, Neira and Barber found that while higher investment in social media (e.g. active social media use) predicted adolescents’ depressive symptoms, no relationship was found between the frequency of social media use and depressed mood [ 28 ].

In the 16 studies, anxiety and depression were the most commonly measured outcome. The prominent risk factors for anxiety and depression emerging from this study comprised time spent, activity, and addiction to social media. In today's world, anxiety is one of the basic mental health problems. People liked and commented on their uploaded photos and videos. In today's age, everyone is immune to the social media context. Some teens experience anxiety from social media related to fear of loss, which causes teens to try to respond and check all their friends' messages and messages on a regular basis.

On the contrary, depression is one of the unintended significances of unnecessary use of social media. In detail, depression is limited not only to Facebooks but also to other social networking sites, which causes psychological problems. A new study found that individuals who are involved in social media, games, texts, mobile phones, etc. are more likely to experience depression.

The previous study found a 70% increase in self-reported depressive symptoms among the group using social media. The other social media influence that causes depression is sexual fun [ 12 ]. The intimacy fun happens when social media promotes putting on a facade that highlights the fun and excitement but does not tell us much about where we are struggling in our daily lives at a deeper level [ 28 ]. Another study revealed that depression and time spent on Facebook by adolescents are positively correlated [ 22 ]. More importantly, symptoms of major depression have been found among the individuals who spent most of their time in online activities and performing image management on social networking sites [ 14 ].

Another study assessed gender differences in associations between social media use and mental health. Females were found to be more addicted to social media as compared with males [ 26 ]. Passive activity in social media use such as reading posts is more strongly associated with depression than doing active use like making posts [ 23 ]. Other important findings of this review suggest that other factors such as interpersonal trust and family functioning may have a greater influence on the symptoms of depression than the frequency of social media use [ 28 , 29 ].

Limitation and suggestion

The limitations and suggestions were identified by the evidence involved in the study and review process. Previously, 7 of the 16 studies were cross-sectional and slightly failed to determine the causal relationship between the variables of interest. Given the evidence from cross-sectional studies, it is not possible to conclude that the use of social networks causes mental health problems. Only three longitudinal studies examined the causal relationship between social media and mental health, which is hard to examine if the mental health problem appeared more pronounced in those who use social media more compared with those who use it less or do not use at all [ 19 , 20 , 24 ]. Next, despite the fact that the proposed relationship between social media and mental health is complex, a few studies investigated mediating factors that may contribute or exacerbate this relationship. Further investigations are required to clarify the underlying factors that help examine why social media has a negative impact on some peoples’ mental health, whereas it has no or positive effect on others’ mental health.

Conclusions

Social media is a new study that is rapidly growing and gaining popularity. Thus, there are many unexplored and unexpected constructive answers associated with it. Lately, studies have found that using social media platforms can have a detrimental effect on the psychological health of its users. However, the extent to which the use of social media impacts the public is yet to be determined. This systematic review has found that social media envy can affect the level of anxiety and depression in individuals. In addition, other potential causes of anxiety and depression have been identified, which require further exploration.

The importance of such findings is to facilitate further research on social media and mental health. In addition, the information obtained from this study can be helpful not only to medical professionals but also to social science research. The findings of this study suggest that potential causal factors from social media can be considered when cooperating with patients who have been diagnosed with anxiety or depression. Also, if the results from this study were used to explore more relationships with another construct, this could potentially enhance the findings to reduce anxiety and depression rates and prevent suicide rates from occurring.

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The authors have declared that no competing interests exist.