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Social Media Addiction, Self-Compassion, and Psychological Well-Being: A Structural Equation Model

Eirini marina mitropoulou, marianna karagianni, christoforos thomadakis.

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Corresponding author:Eirini Marina Mitropoulou✉ [email protected]

Cite this article as: Mitropoulou EM, Karagianni M, Thomadakis C. Social media addiction, self-compassion, and psychological well-being: A structural equation model. Alpha Psychiatry. 2022; 23(6) :298-304.

Received 2022 May 26; Accepted 2022 Sep 26; Collection date 2022 Nov.

Research indicates that social media addiction is associated with several psychological consequences, for example, depression. Distressed individuals tend to devote more time to social media, which leads to impairment of daily life. Interestingly, individuals feeling more compassionate toward them tend to devote less time to social media and feel less psychologically distressed. This research aimed to examine the association between social media addiction and self-compassion and whether it can be further explained through the association of psychological distress.

A sample of 255 Greek adults received a personal invitation sent to various social media platforms. Invitations included a link, which redirected participants to the information sheet and the study questionnaires, namely the Bergen Social Media Addiction Scale, the Self-Compassion Scale, and the Depression, Anxiety, Stress Scale. Participation was voluntary and no benefit/reward was granted.

As predicted, social media addiction was found to negatively correlate with self-compassion and positively with distress. We used structural equation modeling to examine associations between variables, with psychological distress acting as a mediator. Examination of estimated parameters in the model revealed statistically significant correlations, except for the positive dimensions of the Self-Compassion Scale, which were found to be insignificantly associated.

Conclusion:

Individuals with higher levels of self-compassion tend to report less social media additive behaviors and distress. The extensive use of social media is related to negative feelings and emotions. Self-compassion is a potential protective factor, while distress is a potential risk factor for social media addiction. Intervention programs dealing with social media addiction should consider the role of self-compassion.

Keywords: Social media addiction, self-compassion, depression, anxiety, stress, factor analysis

Main Points

Self-compassionate individuals exhibit social media additive behaviors to a lesser degree than non-self-compassionate individuals.

Only the negative facets of self-compassion (i.e., self-judgment, isolation, and over-identification) actually influence social media addiction, as opposed to the positive ones (i.e., mindfulness, self-kindness, and common humanity), which exhibited nonsignificant relations.

Psychological distress, namely depression, anxiety, and stress, is significantly associated with social media addiction.

Psychological distress is a significant risk factor (mediator) for exhibiting social media addiction.

Introduction

The use of the social media (SM) has increased considerably in the recent years; more than 1 billion individuals worldwide use one or more of these services on a regular basis. 1 Social media applications are virtual communities where users can create their personal profile and make it publicly available, interact with real-life friends, and meet new people, based on shared interests. Despite the benefits of SM to everyday life (e.g., social interaction, marketing enhancement, and information processing), problematic use of SM, referred as SM addiction, has also been documented. 2 According to Andreassen and Pallesen, 3 SM addiction is defined as a psychological dependence on SM use and portrays strong and uncontrollable intrinsic motivation, which leads individuals to devote significant amount of time and effort to SM. This disposition impairs important areas of their daily life, like social activities, academic and/or professional commitments, interpersonal relationships, and psychological health. Although SM addiction has not yet been endorsed as a clinical disorder, the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), has included internet gaming disorder as an emerging issue for further research. 4 Social media addiction has been linked to behaviors akin to other similar addictive disorders, due to the excessive and additive use of SM apps. 2 To our knowledge, SM addiction remains an unexplored public health issue among Greek adults.

Social Media Addiction as a Significant Psychological Health Issue Worldwide

Social media addiction among adolescents and adults is reported with variations in several countries. Robust epidemiological studies, using nationally representative large-scale samples, reported variations in the prevalence rates of SM intense use in the general population. For example, prevalence rate around 4.5% has been reported among 5961 Hungarians. 5 In Germany, 4.1% of males and 3.6% of female adolescents have been demonstrating SM addiction. 6 De Cock et al 7 reported that 2.9% of the Belgium population is classified as SM addicted. To a similar extent, 11.9% of the 23 532 Norwegian population, aged 18-88 years old, were found to report SM addiction. 3 Moreover, studies that used non-probability sampling techniques have reported even higher prevalence rates of SM addiction among specific age groups. For example, the prevalence rates among 2198 Dutch adolescents aged 10-17 years old are approximately 7%. 8 These rates increase by 18% among young Facebook (n = 667) and YouTube (n = 1056) Malaysian users, under the age of 25 9 and by 29.5% among 1110 students aged 18-25 years old, attending a major university in Singapore. 10

Social media addiction has been found to be associated with a range of negative health outcomes among adolescents. In their systematic review, Seabrook et al 11 found a significant association between the quality of SM interaction and mental health. Particularly, individuals who exhibit excessive use of SM tend also to exhibit symptoms of depression, display problematic interaction patterns, feel more vulnerable to peer victimization, and express feelings of disengagement from daily life. All these behaviors are considered a potential risk factor for suicidal desire. Research has also associated SM addiction with disturbances in sleep behaviors, which lead to bedtime and rising time postponements, 12 increased feelings of social distancing, loneliness and anxiety, 13 deterioration of well-being, and social interaction overload, namely the individual’s engagement in social exchange beyond his/her communicative and cooperative capabilities. 14 Identifying factors associated with SM app interaction is highly warranted as guideline for the design of interventions to reduce SM addiction among adolescents and adults.

Self-compassion and Social Media Addiction

The concept of self-compassion has drawn interest, due to its strong link to psychological well-being. 6 Self-compassion pertains to the understanding, acknowledgment, and transformation of personal suffering through self-kindness, self-acceptance, and mindfulness. The concept of self-compassion is identified by 3 interactive components, all having 2 opposite facets. The first facet includes the dimensions of self-judgment and self-kindness, which refer to one’s ability to be caring and understanding toward the self rather than being harsh and self-critical under negative circumstances. Self-compassionate individuals embrace difficult situations, while self-judgmental individuals become easily upset with themselves. The second facet pertains to common humanity and isolation. Individuals exhibiting feelings of isolation tend to be more prone to social distancing and to personal failure inefficiency. The third facet includes the dimensions of mindfulness and over-identification, which pertain to the awareness and acceptance of one’s painful and stressful experiences in a balanced way. Individuals exhibiting mindfulness pay attention to the present moment and accept their thoughts, feelings, and senses. 15

Self-compassion is reported to enhance emotional well-being, 16 reduce feelings of shame-proneness, 17 increase motivation toward personal growth, mitigate health-related problematic behaviors, such as smoking, 15 and reduce feelings of depression and anxiety. 18 , 19 Self-compassion is also reported to have a regulative causal effect on negative feelings and behaviors; individuals, who embrace life and avoid maladaptive beliefs or negative cognitions are less prone to psychological distressful feelings and behaviors. 20

Self-compassion is also associated with SM app usage. 20 Individuals exhibiting higher levels of self-compassion spend less time on social networking, log in to various SM platforms less frequently, report fewer symptoms of intensity, and generally are more positively inclined to online networking interactions. Research has examined the moderating effect of self-compassion on SM addiction, mostly focusing on the effect it pertains to negative body image perceptions, 21 perfectionistic self-presentations, 22 or specific distressful symptoms, such as depression. 20 Social media app users, who exhibit higher levels of self-compassion, tend to report less symptoms of depression than those with lower levels of self-compassion, thus confirming the mediating effect of the latter on SM addiction and psychological distress.

Psychological Distress and Social Media Addiction

The literature reports that social networking has an effect on psychological distress of the users and can become additive. 2 , 23 Psychological distress is identified by 3 components, namely depression, anxiety, and stress. 24 Depression is associated with feelings of hopelessness, self-deprecation, anhedonia, and lack of interest. Anxiety pertains to the autonomic arousal, and the subjective experience of anxious affect and stress is the persistent feeling of tension and/or the excessive worrying in general life situations. Psychological distress is considered an important risk factor to SM addiction. Preliminary research has associated higher levels of depression, anxiety, and stress with Facebook and Instagram excessive use 18 , 25 ; SM-addicted users exhibit withdrawal, poor planning abilities, tolerance to SM app use, preoccupation, impairment of control, and excessive online time.

Psychological distress is reported to have an indirect causal effect on negative feelings and behaviors 18 ; individuals, who feel abandoned, hopeless, and dissatisfied by their lives are more prone to excessive SM exposure.

Research Objective Overview

The present study extends previous research 18 , 20 by exploring the relation between SM addiction and self-compassion to a non-clinical sample of Greek adults. Moreover, we examine whether psychological distress (including all 3 facets, namely depression, anxiety, and stress) moderates the relationship between SM addiction and self-compassion. We therefore hypothesize the following:

Social media addiction will be negatively related to self-compassion.

Psychological distress will be positively related to SM addiction and negatively related to self-compassion.

Psychological distress will moderate the relationship between SM addiction and self-compassion, with stronger association being found by individuals that exhibit lower levels of self-compassion.

Additionally, our research further aims to gather information about the SM habits of Greek adults with regard to SM app usage, which may be considered as directives for healthy SM daily use and assist the design of effective intervention programs for SM addictive behaviors in the future.

Participants

Two hundred fifty-five (n = 255) participants were recruited via snowball sampling procedure. The research was conducted in Greece, and data collection took place between April and May of 2022. Individuals needed to fulfill the study’s inclusion criteria prior to participation. The inclusion criteria were the following: participants should be over 18 years old and should have an active profile account on at least 1 SM app (e.g., Facebook, Instagram, Viber, and Pinterest). Individuals who were under 18 years old and/or did not have an active SM account on any SM platform were excluded from participation. Research participants reported a mean age of 27 years (SD = 8.93), ranging between 18 and 60 years; 176 (69%) were females and approximately half of the participants were university students. Missing values were found only in certain demographics (namely age, education, and occupational status). These missing values do not exceed the 2% of the research sample and are not used in any stage of analysis conducted in this study.

Participants responded to a personal invitation posted on various SM platforms (e.g., Facebook, Twitter, and Instagram), asking them to participate in a study about the impact of SM use, self-compassion, and psychological well-being. Participants were recruited on a volunteer basis through several, different SM online posts, referring to the survey link, posted by the researchers. Participants were also enhanced to invite their friends (sharing similar characteristics) to take part in the research, by sharing the online survey link via their SM profile. The link directed all individuals to the information sheet, which contained research information, along with the first author’s contact details. Prior to participation, individuals provided informed consent for participation; after consent, participants were redirected to the research questionnaires. The survey took approximately 10 minutes for completion. Participation was voluntary, and no benefits or rewards were offered for participating. Participants were also asked to provide demographic information (gender, age, education, and occupation status) and habits of SM use and specifically in which SM they have an active account/profile. Ethical approval was granted by the Ethics Committee of the University of Crete (protocol no. 117/2022).

Bergen Social Media Addiction Scale

The Bergen Social Media Addiction Scale (BSMAS) contains 6 self-report items reflecting core addiction elements (i.e., salience, mood modification, tolerance, withdrawal, conflict, and relapse). 3 Each item is answered with regard to SM experience within a time frame of 12 months and is answered on a 5-point Likert scale ranging from 1 (very rarely) to 5 (very often). A score of ≥3 is an indication of SM tendency to addiction. Sample item is “How often during the last year have you used SM so much that it has a negative impact on your job/studies?" The original BSMAS is provided in English, and hence adaptation to Greek was required; adaptation was based on the committee translation process. 26 Three bilingual experts translated the original measure into Greek, with the translations subsequently reevaluated by 1 additional expert, who acted as a verifier. Internal consistency of scale was ω  = 0.83. Confirmatory factor analysis showed excellent fit to the data [ χ 2 (9, n = 255) = 20.0, comparative fit index (CFI) = 0.98, Tucker–Lewis index (TLI) = 0.96, root mean square error of approximation (RMSEA) = 0.07, standardized root mean residual (SRMR) = 0.03]. The adapted measure is provided in Appendix 1.

The Self-Compassion Scale

The Self-Compassion Scale (SCS) assesses a person’s feelings of compassion for oneself during times of distress and disappointment. 27 The SCS consists of 26 items and utilizes a 5-point Likert-type scale, with responses ranging from 1 (almost never) to 5 (almost always). Sample items include “I’m tolerant of my own flaws and inadequacies" (i.e., self-kindness) and “When something upsets me, I try to keep my emotions in balance" (i.e., mindfulness). The items included in each subscale, along with reported internal consistency indices, are as follows: self-kindness (5 items; α  = 0.70), self-judgment (5 items; α  = 0.77), common humanity (4 items; α  = 0.72), isolation (4 items; α  = 0.71), mindfulness (4 items; α  = 0.72), and over-identification (4 items; α  = 0.76). The SCS has been adopted to Greek by Mantzios et al. 28 and its reported internal consistency reliability was α  = 0.87.

The Depression Anxiety and Stress Scale

The Depression Anxiety and Stress Scale-21 (DASS-21) contains 3 self-report scales designed to measure how frequently individuals experience symptoms of depression, anxiety, and stress during the last week. 24 The DASS-21 consists of 21 items, with each subscale having 7 items; responses use a 4-point Likert scale ranging from 0 (did not apply to me at all) to 3 (applied to me very much or most of the time). Sample items include “I tended to over-reach to situations" (stress) and “I felt that life was meaningless" (depression). The DASS-21 has been adopted to Greek by Lyrakos et al. 29 and the scale’s overall internal consistency reliability was Cronbach’s α = 0.79.

Data analysis was conducted using Jamovi Statistical Computer Software version 2.2 (The Jamovi project; Sydney, Australia). Descriptive statistics and non-zero correlations among all variables were examined. Data normality was evaluated with the Shapiro–Wilk normality test; significance values > .05 indicate normally distributed data. 30 Confirmatory factor analysis was initially conducted to examine the adequacy of the measurement for all constructs under investigation. Structural equation modeling was then performed to examine the fit of the hypothesized structural model. 31 To examine the mediation effect of psychological distress on self-compassion and SΜ addiction, a bootstrap procedure was used to test the indirect effect 32 using a bias-corrected confidence interval of 2000 resamples. To evaluate the proposed model, the method of the diagonal weighted least squares estimator was used, which is considered to outperform other relevant estimators (e.g., maximum likelihood estimator) and is considered fairly accurate with small size samples, non-normality, and few model parameters. 33

To assess model adequacy, several goodness-of-fit indices have been assessed and reported in combination, such as the χ 2 fit index, the CFI, the TLI, the RMSEA, and the SRMR. 34 A nonsignificant P -value indicates good fit for the χ 2 fit index. 34 However, chi-square is sensitive to sample size (with smaller samples indicating statistically significant outputs); thus, the calculation of the chi-square index to the respective degrees of freedom ( χ 2 /df) is preferred, with a ratio of ≤2 indicating good fit. In addition, CFI and TLI range between 0 and 1, with values >0.90 indicating adequate fit. A value below 0.05 in RMSEA and SRMR indicates an excellent fit, with values ranging from 0.05 to 0.08 indicating a reasonable fit. 33 , 35

In terms of SM use, 12 (4.7%) participants reported that they spend ≥5 hours on SM apps online per day and 186 (73.1%) use more than 5 different SM platforms daily (see Table 1 ). The most frequently reported SM platforms are the Facebook/Messenger and the Instagram (98.0% and 88.2% respectively), followed by the Viber (78.0%), the YouTube (67.5%), and the Pinterest (36.9%). Frequencies and percentages of age, educational level, occupational status, and the number of SM platforms use are presented in Table 1 . Due to the snowball sampling procedure used for data collection, participants are unequally distributed in accordance to their age group and their educational level.

Frequency Table for Demographic and Social Media Habits

Focusing further on the SM addiction among Greek adults, the assessed mean level of the BSMAS in our sample was estimated and perceived as relatively low ( M  = 10.2, SD = 4.21, range: 6-25). Although the Greek version of the BSMAS has not yet been examined for having critical cutoff scores on SM addiction, we employed the critical cutoff scores suggested by Andreassen et al 12 These cutoff scores were reached by a relatively low percentage of the Greek sample (polythetic scoring: 2%; monothetic scoring: 16.1%), which indicates low levels of SM addiction.

Table 2 presents the correlations between all variables included in the study. Results revealed a negative correlation between SM addiction and self-compassion. Findings also revealed a positive association between psychological distress and SM addiction and a negative relation between psychological distress and self-compassion, thus confirming our first and second research hypothesis.

Correlations Between Variables Included in the Study

n = 255; BSMAS, Bergen Social Media Addiction Scale; DASS-21, Depression Anxiety and Stress Scale; SCS, Self-Compassion Scale.

* P < .05, ** P < .01, *** P < .001.

The full structural equation model was put to the test, to examine the mediation effect of psychological distress between self-compassion and SM addiction. The model revealed a good fit to our data [ χ 2 (87) = 177, P < .001, CFI = 0.95, TLI = 0.94, SRMR = 0.08, RMSEA = 0.06 (CI 0.05-0.08)], yet the standardized factor loadings for the SCS variables were found to be statistically nonsignificant. Inspection of the subscales’ correlations revealed insignificant criterion correlations only for the positive dimensions of the self-compassion variable. Specifically, the subscales of mindfulness, common humanity, and self-kindness exhibited very low or insignificant correlations to psychological distress and SM addiction ( Table 2 ). The standardized path coefficients of the structural model are presented in Figure 1 .

Structural equation model of self-compassion, psychological distress, and social media addiction. * P < .05, ** P < .001.

According to Neff, 27 the SCS examines 3 basic and interactive components, which pertains 2 opposite dimensions. Results revealed criterion correlations in the expected direction only for those dimensions/subscales that followed a negatively skewed response format (self-judgment, isolation, and over-identification). In addition, insignificant correlations were found for the positively skewed response subscales, namely the self-kindness, the common humanity, and the mindfulness subscale. Therefore, it was decided to test a modified structural equation model, including only the negatively keyed SCS facets. This modified measurement model yielded a significantly better fit to our data [ χ 2 (51) = 35.1, P < .001, CFI = 1.00, TLI = 0.98, SRMR = 0.05, RMSEA = 0.00 (CI 0.00-0.03)] than the original SEM.

The tested mediational model predicted SM addiction. Table 3 presents the factor loadings of the modified structural equation model; standardized factor loadings ranged from 0.46 to 0.94 and were all statistically significant at P < .001. Self-compassion was found to have a significant direct effect on SM addiction and a significant indirect effect through psychological distress (see Figure 1 ). In sum, psychological distress is found to negatively correlate with self-compassion and positively relate to SM addiction and is perceived as a mediator among those concepts.

Unstandardized and Standardized Loadings for the Measurement Model (Including only the 3 Negative Subscales)

The present study investigated the link between SM addiction, self-compassion, and psychological distress, using data from Greek adults. As proposed, higher levels of self-compassion were associated with lower levels of SM app usage. These findings give prominence to the first hypothesis, since self-compassion strengthens individuals from expressing SM addiction and/or mental health problems. 18 , 36 Individuals who demonstrate higher levels of self-compassion tend to spend less time on SM apps and overall feel healthier.

Furthermore, our research findings indicated that psychological distress appeared to have an effect on SM addiction; individuals feeling highly distressed also tend to report higher levels of SM app addiction. This positive inclination to SM addiction was significant for all 3 negative psychological states, namely depression, anxiety, and stress. The findings from our study are similar to those reported from previous researches 2 , 18 ; prolonged exposure to SM platforms is associated with certain negative psychological consequences. Nonetheless, our findings expand our understanding on SM addiction, since results are not focused on a specific SM platform, or on a single SM user profile. Participants of this study were asked to report general tendencies toward all SM indiscriminately; therefore, inferences can be made from a general perspective.

Exploration of the mediation effect between the full spectrum of self-compassion, SM addiction, and psychological distress failed to explain how self-compassion enables a positive relation. Interestingly, examination of certain facets of self-compassion revealed that the concept’s negative counterparts, namely self-judgment, isolation, and over-identification, exhibit strong associations between SM addiction and psychological distress; in conjunction with previous findings, 18 only these negative facets are moderated by psychological distress.

With regard to our additional objective, current findings allow only statements about tendencies of Greek population toward SM addiction. Although the adopted BSMAS has not yet been examined for having critical cutoff scores on SM intensity, the critical cutoff scores suggested by Andreassen et al 12 were used. These cutoff scores were reached by a relatively low percentage of the Greek sample, indicating low levels of SM addiction. Intervention programs, promoting psychological well-being and coping with SM addiction, need to focus on the control, the acceptance, and the regulation of the self. 15 Our research findings shed light on the unexplored behavioral patterns regarding the use of SM and self-compassion, by emphasizing on the negative dimensions of self-compassion. The emphasis on the negative facets may be highly beneficial for establishing new intervention programs. For example, individuals who get easily upset with themselves during difficult life situations (or generally get easily stressed) tend to reveal symptoms of SM addiction more frequently. Conversely, individuals who understand how to fairly and honestly judge the time they spent on SM may be more aware of their potential problematic behavior and more stimulated to overcome such negative behaviors by themselves. Likewise, when individuals exhibit feelings of isolation to a greater extent, they feel more prone to get socially distanced and lessen the time they spend on SM, or effectively manage social interaction overload. 14 On the contrary, individuals who feel more acceptive of their thoughts and feelings are prone to excessive SM behavioral patterns and increase their possibility for expressing SM addiction.

The results from the present study should be interpreted in light of certain limitations. Firstly, only self-reported measures were used. Consequently social desirability is an issue that needs to be further addressed; results may have underestimated the prevalence of SM addiction among Greek adults. Secondly, the data used are not representative of the Greek population; males are underrepresented, and age, occupation status, and gender are presented unequally. It should also be noted that a large number of individuals use SM apps outside the range of the age or the occupational status included in our study. Thirdly, participants in this study have been recruited from self-selected processes, through snowball sampling procedures. Therefore, inferences on individuals’ SM additive behaviors may not be representative of the broader Greek population. Finally, research design did not account for information regarding problematic smartphone usage. 37 Smartphone and SM addiction are highly overlapping, since smartphones are predominantly used for social networking purposes. This lack of distinction between the smartphone and the SM addiction confounds our findings and affects the generalizability of our results.

Overall, the findings add to the existing literature by examining the relation and the mediation effect between self-compassion, SM addiction, and psychological distress. Results showed that psychological distress is a potential risk factor, while self-compassion is a potential protective factor for SM addiction. The mediating role of self-compassion is not easily endorsed, and more complex patterns of interventions toward the confinement of SM addiction need to be further established. Strengthening self-compassion and psychological well-being are potentially important components to be considered in social network addiction interventions targeting adults.

Ethics Committee Approval: Ethical committee approval was received from the Ethics Committee of the University of Crete (Approval No: 117/2022).

Informed Consent: Written informed consent was obtained from all participants who participated in this study.

Peer-review: Externally peer-reviewed.

Author Contributions: Concept – E.M.; Design – E.M., M.K.; Supervision – E.M.; Funding – E.M., M.K., C.T.; Materials – E.M.; Data Collection and/or Processing – E.M.; Analysis and/or Interpretation – E.M., C.T.; Literature Review – E.M.; Writing Manuscript – E.M., C.T.; Critical Review – E.M., M.K.

Declaration of Interests: The authors have no conflicts of interest to declare.

Funding: The authors declared that this study has received no financial support.

Bergen Social Media Addiction Scale Greek version

Παρακάτω ακολουθούν ορισμένες ερωτήσεις σχετικά με τη σχέση σας με τα μέσα κοινωνικής δικτύωσης και τι κάνετε με αυτά (όπως είναι για παράδειγμα το Facebook, το Instagram, κ.α.). Για κάθε ερώτηση επιλέξτε την απάντηση που σας περιγράφει καλύτερα.

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A review of theories and models applied in studies of social media addiction and implications for future research

Affiliations.

• 1 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
• 2 School of Information, The University of Texas at Austin, USA. Electronic address: [email protected].
• PMID: 33268185
• DOI: 10.1016/j.addbeh.2020.106699

With the increasing use of social media, the addictive use of this new technology also grows. Previous studies found that addictive social media use is associated with negative consequences such as reduced productivity, unhealthy social relationships, and reduced life-satisfaction. However, a holistic theoretical understanding of how social media addiction develops is still lacking, which impedes practical research that aims at designing educational and other intervention programs to prevent social media addiction. In this study, we reviewed 25 distinct theories/models that guided the research design of 55 empirical studies of social media addiction to identify theoretical perspectives and constructs that have been examined to explain the development of social media addiction. Limitations of the existing theoretical frameworks were identified, and future research areas are proposed.

Keywords: Facebook addiction; Internet addiction; Literature review; Problematic use; Social media addiction; Theoretical framework.

Copyright © 2020 Elsevier Ltd. All rights reserved.

Publication types

• Behavior, Addictive*
• Internet Addiction Disorder
• Interpersonal Relations
• Social Media*

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.

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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.

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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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Social Networking Sites and Addiction: Ten Lessons Learned

Daria j kuss, mark d griffiths.

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Correspondence: [email protected] ; Tel.: +44-115-8484153

Received 2017 Feb 1; Accepted 2017 Mar 13; Issue date 2017 Mar.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/ ).

Online social networking sites (SNSs) have gained increasing popularity in the last decade, with individuals engaging in SNSs to connect with others who share similar interests. The perceived need to be online may result in compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn. Recommendations for research and clinical applications are provided.

Keywords: social networking sites, addiction, social media, FOMO, nomophobia, smartphone addiction, microblogging, gaming, dating, recommendations

1. Introduction

The history of social networking sites (SNSs) dates back to 1997, when the first SNS SixDegrees emerged as a result of the idea that individuals are linked via six degrees of separation [ 1 ], and is conceived as “the small world problem” in which society is viewed as becoming increasingly inter-connected [ 2 ]. In 2004, Facebook , was launched as an online community for students at Harvard University and has since become the world’s most popular SNS [ 3 ]. In 2016, there were 2.34 billion social network users worldwide [ 4 ]. In the same year, 22.9% of the world population used Facebook [ 5 ]. In 2015, the average social media user spent 1.7 h per day on social media in the USA and 1.5 h in the UK, with social media users in the Philippines having the highest daily use at 3.7 h [ 6 ]. This suggests social media use has become an important leisure activity for many, allowing individuals to connect with one another online irrespective of time and space limitations.

It is this kind of connecting or the self-perceived constant need to connect that has been viewed critically by media scholars. Following decades of researching technology-mediated and online behaviors, Turkle [ 7 ] claims overreliance on technology has led to an impoverishment of social skills, leaving individuals unable to engage in meaningful conversations because such skills are being sacrificed for constant connection, resulting in short-term attention and a decreased ability to retain information. Individuals have come to be described as “alone together”: always connected via technology, but in fact isolated [ 8 ]. The perceived need to be online may lead to compulsive use of SNSs, which in extreme cases may result in symptoms and consequences traditionally associated with substance-related addictions. Since the publication of the first ever literature review of the empirical studies concerning SNS addiction in 2011 [ 3 ], the research field has moved forward at an increasingly rapid pace. This hints at the scientific community’s increasing interest in problematic and potentially addictive social networking use. In order to present new insights into online social networking and addiction, in this paper, 10 lessons learned concerning online social networking sites and addiction based on the insights derived from recent empirical research will be presented. These are: (i) social networking and social media use are not the same; (ii) social networking is eclectic; (iii) social networking is a way of being; (iv) individuals can become addicted to using social networking sites; (v) Facebook addiction is only one example of SNS addiction; (vi) fear of missing out (FOMO) may be part of SNS addiction; (vii) smartphone addiction may be part of SNS addiction; (viii) nomophobia may be part of SNS addiction; (ix) there are sociodemographic differences in SNS addiction; and (x) there are methodological problems with research to date. These are discussed in turn.

2. 10 Lessons Learned from Recent Empirical Literature

2.1. social networking and social media use are not the same.

Social networking and social media use have often been used interchangeably in the scientific literature. However, they are not the same. Social media refers to the web 2.0 capabilities of producing, sharing, and collaborating on content online (i.e., user-generated content, implying a social element). Accordingly, social media use includes a wide range of social applications, such as collaborative projects, weblogs, content communities, social networking sites, virtual game worlds, and virtual social worlds [ 9 ], each of which will be addressed below.

Collaborative projects can be shared and worked on jointly and simultaneously using cloud-based computing. Two different types can be distinguished: Wikis allow for creating, removing and modifying online content (e.g., Wikipedia ). Social bookmarking applications, on the other hand, allow for numbers of people to accumulate and appraise websites (e.g., Delicious ). Taken together, collaborative projects may produce a superior end result in comparison to individual projects [ 9 ], which can be linked to the concept of collective intelligence, whereby the intelligence in the group is greater than the sum of its parts [ 10 ].

Weblogs (or “blogs”) can also be considered social media. Blogs allow individuals to share personal online diaries and information (sometimes in the form of images and videos), which may or may not be commented upon by other internet users. Next, there are content communities and video-sharing sites (e.g., YouTube ). Content may include videos, but also text (e.g., BookCrossing ), photographs (e.g., Instagram ), and PowerPoint presentations (e.g., Slideshare ), and in most cases, there is no a need for individuals to have personal profiles, and if they do, these tend to include limited personal information. Virtual game worlds allow users to create an online alter ego in the form of an avatar and to play with other players in large gaming universes (and the next section covers gaming in more detail). Kaplan and Haenlein [ 9 ] differentiate these from virtual social worlds from virtual game worlds, whereby the former allow individuals to create online characters which live in an alternative virtual world that is similar to their real life environments on the one hand, but defies physical laws. Arguably the best example of these virtual social worlds is Second Life , populated by human-like avatars, who engage in activities users engage in on an everyday basis, such as furnishing houses, going shopping, and meeting friends.

Finally, there are social networking sites, which we have previously defined as “virtual communities where users can create individual public profiles, interact with real-life friends, and meet other people based on shared interests” ([ 3 ]; p. 3529). Social networking is particularly focused on connecting people, which does not apply to a number of the other social media applications outlined above. Engaging in social networking comprises a specific type of social media use, therefore they are not synonymous. Consequently, studies that have examined social media addiction and social networking addiction may also be using the terms interchangeably, suggesting nosological imprecision.

2.2. Social Networking Is Eclectic

Despite social networking being one type of social media use (as outlined in the previous section), the behavior is inherently eclectic because it includes a variety of apps and services that can be engaged in. For instance, social networking can be the use of traditional social networking sites, such as Facebook. Facebook can be considered an ‘egocentric’ SNS (rather than the previously more common virtual communities that focused on shared interests between members) because it allows individuals to represent themselves using individual profiles and wall posts. These can contain text and audiovisual content, whilst connecting to friends who often appear as real life friends and acquaintances given the main motivation of individuals to use SNSs such as Facebook is to maintain their connections [ 3 ].

In 2016, the most popular social networking site was Facebook with 1712 million active users [ 5 ]. Facebook has long established its supremacy in terms of active members, with membership numbers steadily increasing by 17%–20% annually [ 11 ]. Facebook is a very active network. Every minute, 510,000 comments are posted; 293,000 statuses are updated; and 136,000 photos are uploaded, whilst the average user spends approximately 20 min daily on the site [ 11 ].

Over the past few years, new networks have emerged that have gradually risen in popularity, particularly amongst younger generations. Instagram was launched in 2010 as a picture sharing SNS, claiming to “allow you to experience moments in your friends’ lives through pictures as they happen” [ 12 ]. In 2016, Instagram had 500 m active users [ 5 ]. Snapchat was launched in 2011 [ 13 ] as an SNS that allows users to message and connect with others using a smartphone and to send texts, videos, and make calls. Snapchat is different from other networks in that it has an inherently ephemeral nature, whereby any messages are automatically deleted shortly after the receiver has viewed them, allowing an increased experience of perceived privacy and safety online [ 14 ]. However, teenagers are especially aware of the transitory nature of Snapchat messages and therefore take screenshots and keep them stored on their mobile phones or in the cloud, simply to have proof of conversations and visuals spread on this medium. The privacy advantage of the medium is thereby countered. Snapchat had 200 million users in 2016 [ 5 ]. In the same year, Snapchat was the most popular SNS among 13–24 year-old adolescents and adults in the USA, with 72% of this group using them, followed by 68% Facebook users, and 66% Instagram users [ 15 ]. The popularity of Snapchat —particularly among young users—suggests the SNS landscape is changing in this particular demographic, with users being more aware of potential privacy risks, enjoying the lack of social pressure on Snapchat as well as the increased amount of control over who is viewing their ephemeral messages. However, it could also be the case that this may lead to the complete opposite by increasing the pressure to be online all the time because individuals risk missing the connecting thread in a continuing stream of messages within an online group. This may be especially the case in Snapchat groups/rooms created for adolescents in school or other contexts. This can lead to decreasing concentration during preparation tasks for school at home, and may lead to constant distraction because of the pressure to follow what is going on as well as the fear of missing out. From a business point of view, Snapchat has been particularly successful due to its novel impermanent approach to messaging, with Facebook founder Mark Zuckerberg offering $3 billion to buy the SNS, which has been declined by Evan Spiegel, Snapchat’s CEO and co-founder [ 13 ]. These facts suggest the world of traditional SNS is changing.

Social networking can be instant messaging. The most popular messaging services to date are WhatsApp and Facebook Messenger with 1000 million active users each [ 5 ]. WhatsApp is a mobile messaging site that allows users to connect to one another via messages and calls using their internet connection and mobile data (rather than minutes and texts on their phones), and was bought by Facebook in 2014 for $22 billion [ 16 ], leading to controversies about Facebook’s data sharing practices (i.e., Whatsapp phone numbers being linked with Facebook profiles), resulting in the European Commission fining Facebook [ 17 ]. In addition to WhatsApp , Facebook owns their own messaging system, which is arguably the best example of the convergence between traditional SNS use and messaging, and which functions as an app on smartphones separate from the actual Facebook application.

Social networking can be microblogging. Microblogging is a form of more traditional blogging, which could be considered a personal online diary. Alternatively, microblogging can also be viewed as an amalgamation of blogging and messaging, in such a way that messages are short and intended to be shared with the writer’s audience (typically consisting of ‘followers’ rather than ‘friends’ found on Facebook and similar SNSs). A popular example of a microblogging site is Twitter , which allows 140 characters per Tweet only. In 2016, Twitter had 313 million active users [ 5 ], making it the most successful microblogging site to date. Twitter has become particularly used as political tool with examples including its important role in the Arab Spring anti-government protests [ 18 ], as well as extensive use by American President Donald Trump during and following his presidential campaign [ 19 ]. In addition to microblogging politics, research has also assessed the microblogging of health issues [ 20 ].

Social networking can be gaming. Gaming can arguably be considered an element of social networking if the gaming involves connecting with people (i.e., via playing together and communicating using game-inherent channels). It has been argued that large-scale internet-enabled games (i.e., Massively Multiplayer Role-Playing Games [MMORPGs]), such as the popular World of Warcraft , are inherently social games situated in enormous virtual worlds populated by thousands of gamers [ 21 , 22 ], providing gamers various channels of communication and interaction, and allowing for the building of relationships which may extend beyond the game worlds [ 23 ]. By their very nature, games such as MMORPGs are “particularly good at simultaneously tapping into what is typically formulated as game/not game, social/instrumental, real/virtual. And this mix is exactly what is evocative and hooks many people. The innovations they produce there are a result of MMOGs as vibrant sites of culture” [ 24 ]. Not only do these games offer the possibility of communication, but they provide a basis for strong bonds between individuals when they unite through shared activities and goals, and have been shown to facilitate and increase intimacy and relationship quality in couples [ 25 ] and online gamers [ 22 , 23 ]. In addition to inherently social MMORPGs, Facebook -enabled games—such as Farmville or Texas Hold “Em Poker ”—can be subsumed under the social networking umbrella if they are being used in order to connect with others (rather than for solitary gaming purposes) [ 26 , 27 ].

Social networking can be online dating. Presently, there are many online dating websites available, which offer their members the opportunity to become part of virtual communities, and they have been especially designed to meet the members’ romantic and relationship-related needs and desires [ 28 ]. On these sites, individuals are encouraged to create individual public profiles, to interact and communicate with other members with the shared interest of finding a ‘date’ and/or long-term relationships, therewith meeting the present authors’ definition of SNS. In that way, online dating sites can be considered social networking sites. However, these profiles are often semi-public, with access granted only to other members of these networks and/or subscribers to the said online dating services. According to the US think tank Pew Research Center’s Internet Project [ 29 ], 38% of singles in the USA have made use of online dating sites or mobile dating applications. Moreover, nearly 60% of internet users think that online dating is a good way to meet people, and the percentage of individuals who have met their romantic partners online has seen a two-fold increase over the last years [ 29 ]. These data suggest online dating is becoming increasingly popular, contributing to the appeal of online social networking sites for many users across the generations. However, it can also be argued that online dating sites such as Tinder may be less a medium for ‘long-term relationships’, given that Tinder use can lead to sexual engagement. This suggests the uses and gratifications perspective underlying Tinder use points more in the direction of other motives, such as physical and sexual aspirations and needs, rather than purely romance.

Taken together, this section has argued that social networking activities can comprise a wide variety of usage motivations and needs, ranging from friendly connection over gaming to romantic endeavors, further strengthening SNS’ natural embeddedness in many aspects of the everyday life of users. From a social networking addiction perspective, this may be similar to the literature on Internet addiction which often delineates between addictions to specific applications on the Internet (e.g., gaming, gambling, shopping, sex) and more generalized Internet addiction (e.g., concerning problematic over-use of the Internet comprising many different applications) [ 30 , 31 ].

2.3. Social Networking Is a Way of Being

In the present day and age, individuals have come to live increasingly mediated lives. Nowadays, social networking does not necessarily refer to what we do, but who we are and how we relate to one another. Social networking can arguably be considered a way of being and relating, and this is supported by empirical research. A younger generation of scholars has grown up in a world that has been reliant on technology as integral part of their lives, making it impossible to imagine life without being connected. This has been referred to as an ‘always on’ lifestyle: “It’s no longer about on or off really. It’s about living in a world where being networked to people and information wherever and whenever you need it is just assumed” [ 32 ]. This has two important implications. First, being ‘on’ has become the status quo. Second, there appears to be an inherent understanding or requirement in today’s technology-loving culture that one needs to engage in online social networking in order not to miss out, to stay up to date, and to connect. Boyd [ 32 ] herself refers to needing to go on a “digital sabbatical” in order not be on, to take a vacation from connecting, with the caveat that this means still engaging with social media, but deciding which messages to respond to.

In addition to this, teenagers particularly appear to have subscribed to the cultural norm of continual online networking. They create virtual spaces which serve their need to belong, as there appear to be increasingly limited options of analogous physical spaces due to parents’ safety concerns [ 33 ]. Being online is viewed as safer than roaming the streets and parents often assume using technology in the home is normal and healthy, as stated by a psychotherapist treating adolescents presenting with the problem of Internet addiction: “Use of digital media is the culture of the household and kids are growing up that way more and more” [ 34 ]. Interestingly, recent research has demonstrated that sharing information on social media increases life satisfaction and loneliness for younger adult users, whereas the opposite was true for older adult users [ 35 ], suggesting that social media use and social networking are used and perceived very differently across generations. This has implications for social networking addiction because the context of excessive social networking is critical in defining someone as an addict, and habitual use by teenagers might be pathologized using current screening instruments when in fact the activity—while excessive—does not result in significant detriment to the individual’s life [ 36 ].

SNS use is also driven by a number of other motivations. From a uses and gratifications perspective, these include information seeking (i.e., searching for specific information using SNS), identity formation (i.e., as a means of presenting oneself online, often more favorably than offline) [ 37 ], and entertainment (i.e., for the purpose of experiencing fun and pleasure) [ 38 ]. In addition to this, there are the motivations such as voyeurism [ 39 ] and cyberstalking [ 40 ] that could have potentially detrimental impacts on individuals’ health and wellbeing as well as their relationships.

It has also been claimed that social networking meets basic human needs as initially described in Maslow’s hierarchy of needs [ 41 ]. According to this theory, social networking meets the needs of safety, association, estimation, and self-realization [ 42 ]. Safety needs are met by social networking being customizable with regards to privacy, allowing the users to control who to share information with. Associative needs are fulfilled through the connecting function of SNSs, allowing users to ‘friend’ and ‘follow’ like-minded individuals. The need to estimate is met by users being able to ‘gather’ friends and ‘likes’, and compare oneself to others, and is therefore related to Maslow’s need of esteem. Finally, the need for self-realization, the highest attainable goal that only a small minority of individuals are able to achieve, can be reached by presenting oneself in a way one wants to present oneself, and by supporting ‘friends’ on those SNSs who require help. Accordingly, social networking taps into very fundamental human needs by offering the possibilities of social support and self-expression [ 42 ]. This may offer an explanation for the popularity of and relatively high engagement with SNSs in today’s society. However, the downside is that high engagement and being always ‘on’ or engaged with technology has been considered problematic and potentially addictive in the past [ 43 ], but if being ‘always on’ can be considered the status quo and most individuals are ‘on’ most of the time, where does this leave problematic use or addiction? The next section considers this question.

2.4. Individuals Can Become Addicted to Using Social Networking Sites

There is a growing scientific evidence base to suggest excessive SNS use may lead to symptoms traditionally associated with substance-related addictions [ 3 , 44 ]. These symptoms have been described as salience, mood modification, tolerance, withdrawal, relapse, and conflict with regards to behavioral addictions [ 45 ], and have been validated in the context of the Internet addiction components model [ 46 ]. For a small minority of individuals, their use of social networking sites may become the single most important activity that they engage in, leading to a preoccupation with SNS use (salience). The activities on these sites are then being used in order to induce mood alterations, pleasurable feelings or a numbing effect (mood modification). Increased amounts of time and energy are required to be put into engaging with SNS activities in order to achieve the same feelings and state of mind that occurred in the initial phases of usage (tolerance). When SNS use is discontinued, addicted individuals will experience negative psychological and sometimes physiological symptoms (withdrawal), often leading to a reinstatement of the problematic behavior (relapse). Problems arise as a consequence of the engagement in the problematic behavior, leading to intrapsychic (conflicts within the individual often including a subjective loss of control) and interpersonal conflicts (i.e., problems with the immediate social environment including relationship problems and work and/or education being compromised).

Whilst referring to an ‘addiction’ terminology in this paper, it needs to be noted that there is much controversy within the research field concerning both the possible overpathologising of everyday life [ 47 , 48 ] as well as the most appropriate term for the phenomenon. On the one hand, current behavioral addiction research tends to be correlational and confirmatory in nature and is often based on population studies rather than clinical samples in which psychological impairments are observed [ 47 ]. Additional methodological problems are outlined below ( Section 2.10 ). On the other hand, in the present paper, the present authors do not discriminate between the label addiction, compulsion, problematic SNS use, or other similar labels used because these terms are being used interchangeably by authors in the field. Nevertheless, when referring to ‘addiction’, the present authors refer to the presence of the above stated criteria, as these appear to hold across both substance-related as well as behavioral addictions [ 45 ] and indicate the requirement of significant impairment and distress on behalf of the individual experiencing it in order to qualify for using clinical terminology [ 49 ], such as the ‘addiction’ label.

The question then arises as what it is that individuals become addicted to. Is it the technology or is it more what the technology allows them to do? It has been argued previously [ 34 , 50 ] that the technology is but a medium or a tool that allows individuals to engage in particular behaviors, such as social networking and gaming, rather than being addictive per se . This view is supported by media scholars: “To an outsider, wanting to be always-on may seem pathological. All too often it’s labelled an addiction. The assumption is that we’re addicted to the technology. The technology doesn’t matter. It’s all about the people and information” [ 32 ]. Following this thinking, one could claim that it is not an addiction to the technology, but to connecting with people, and the good feelings that ‘likes’ and positive comments of appreciation can produce. Given that connection is the key function of social networking sites as indicated above, it appears that ‘social networking addiction’ may be considered an appropriate denomination of this potential mental health problem.

There are a numbers of models which offer explanations as to the development of SNS addiction [ 51 ]. According to the cognitive-behavioral model, excessive social networking is the consequence of maladaptive cognitions and is exacerbated through a number of external issues, resulting in addictive use. The social skill model suggests individuals use SNSs excessively as a consequence of low self-presentation skills and preference for online social interaction over face-to-face communication, resulting in addictive SNS use [ 51 ]. With respect to the socio-cognitive model, excessive social networking develops as a consequence of positive outcome expectations, Internet self-efficacy, and limited Internet self-regulation, leading to addictive SNS use [ 51 ]. It has furthermore been suggested that SNS use may become problematic when individuals use it in order to cope with everyday problems and stressors, including loneliness and depression [ 52 ]. Moreover, it has been contended that excessive SNS users find it difficult to communicate face-to-face, and social media use offers a variety of immediate rewards, such as self-efficacy and satisfaction, resulting in continued and increased use, with the consequence of exacerbating problems, including neglecting offline relationships, and problems in professional contexts. The resultant depressed moods are then dealt with by continued engagement in SNSs, leading to a vicious cycle of addiction [ 53 ]. Cross-cultural research including 10,930 adolescents from six European countries (Greece, Spain, Poland, the Netherlands, Romania, and Iceland) furthermore showed that using SNS for two or more hours a day was related to internalizing problems and decreased academic performance and activity [ 54 ]. In addition, a study using a sample of 920 secondary school students in China indicated neuroticism and extraversion predicted SNS addiction, clearly differentiating individuals who experience problems as a consequence of their excessive SNS use from those individuals who used games or the Internet in general excessively [ 55 ], further contributing to the contention that SNS addiction appears to be a behavioral problem separate from the more commonly researched gaming addiction. In a study using a relatively small representative sample of the Belgian population (n = 1000), results suggested 6.5% were using SNSs compulsively, with this group having lower scores on measures of emotional stability and agreeableness, conscientiousness, perceived control and self-esteem, and higher scores on loneliness and depressive feelings [ 56 ].

2.5. Facebook Addiction Is Only One Example of SNS Addiction

Over the past few years, research in the SNS addiction field has largely focused on a potential addiction to using Facebook specifically, rather than other SNSs (see e.g., [ 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 ]). However, recent research suggests individuals may develop addiction-related problems as a consequence of using other SNSs, such as Instagram [ 66 ]. It has been claimed that users may experience gratification through sharing photos on Instagram , similar to the gratification they experience when using Facebook , suggesting that the motivation to share photos can be explained by uses and gratifications theory [ 66 , 67 ]. This may also be the reason for why individuals have been found to be less likely to experience addiction-related symptoms when using Twitter in contrast to Instagram [ 66 ]. In addition to the gratification received through photo sharing, these websites also allow to explore new identities [ 68 ], which may be considered to contribute to gratification, as supported by previous research [ 69 ]. Research has also suggested that Instagram use in particular appears to be potentially addictive in young UK adults [ 66 ], offering further support for the contention that Facebook addiction is only one example of SNS addiction.

Other than the presence and possible addictive qualities of SNSs other than Facebook , it has been contended that the respective activities which take place on these websites need to be considered when studying addiction [ 70 ]. For instance, Facebook users can play games such as Farmville [ 36 ], gamble online [ 71 ], watch videos, share photos, update their profiles, and message their friends [ 3 ]. Other researchers have moved beyond the actual website use that is referred to in these types of addictions, and specifically focused on the main activities individuals engage in, referring to constructs such as ‘e-communication addiction’ [ 72 ]. It has also been claimed the term ‘ Facebook addiction’ is already obsolete as there are different types of SNSs that can be engaged in and different activities that can take place on these SNSs [ 70 ]. Following this justified criticism, researchers who had previously studied Facebook addiction specifically [ 58 ] have now turned to studying SNS addiction more generally instead [ 73 ], demonstrating the changing definitional parameters of social networking in this evolving field of research.

2.6. Fear of Missing Out (FOMO) May Be Part of SNS Addiction

Recent research [ 74 , 75 ] has suggested that high engagement in social networking is partially due to what has been named the ‘fear of missing out’ (FOMO). FOMO is “a pervasive apprehension that others might be having rewarding experiences from which one is absent” [ 76 ]. Higher levels of FOMO have been associated with greater engagement with Facebook , lower general mood, lower wellbeing, and lower life satisfaction, mixed feelings when using social media, as well as inappropriate and dangerous SNS use (i.e., in university lectures, and or whilst driving) [ 76 ]. In addition to this, research [ 77 ] suggests that FOMO predicts problematic SNS use and is associated with social media addiction [ 78 ], as measured with a scale adapted from the Internet Addiction Test [ 79 ]. It has been debated whether FOMO is a specific construct, or simply a component of relational insecurity, as observed for example with the attachment dimension of preoccupation with relationships in research into problematic Internet use [ 80 ].

In one study using 5280 social media users from several Spanish-speaking Latin-American countries [ 74 ] it was found that FOMO predicts negative consequences of maladaptive SNS use. In addition, this study also found that the relationship between psychopathology (as operationalized by anxiety and depression symptoms and assessed via the Hospital Anxiety and Depression Scale) and negative consequences of SNS use were mediated by FOMO, emphasizing the importance of FOMO in the self-perceived consequences of high SNS engagement. Moreover, other research [ 75 ] using 506 UK Facebook users has found that FOMO mediates the relationship between high SNS use and decreased self-esteem. Research with psychotherapists working with clients seeking help for their Internet use-related behaviors also suggested that young clients “fear the sort of relentlessness of on-going messaging (…). But concurrently with that is an absolute terror of exclusion” [ 34 ]. Taken together, these findings suggest FOMO may be a significant predictor or possible component of potential SNS addiction, a contention that requires further consideration in future research. Further work is needed into the origins of FOMO (both theoretically and empirically), as well as research into why do some SNS users are prone to FOMO and develop signs of addictions compared to those who do not.

2.7. Smartphone Addiction May Be Part of SNS Addiction

Over the last decade, research assessing problematic and possibly addictive mobile phone use (including smartphones) has proliferated [ 81 ], suggesting some individuals may develop addiction-related problems as a consequence of their mobile phone use. Recent research has suggested problematic mobile phone use is a multi-faceted condition, with dependent use being one of four possible pathways, in addition to dangerous, prohibited, and financially problematic use [ 82 ]. According to the pathway model, an addictive pattern of mobile phone use is characterized by the use of specific applications, including calls, instant messaging, and the use of social networks. This suggests that rather than being an addictive medium per se , mobile technologies including smartphones and tablets are media that enable the engagement in potentially addictive activities, including SNS use. Put another way, it could be argued that mobile phone addicts are no more addicted to their phones than alcoholics are addicted to bottles.

Similarly, it has been argued previously that individuals do not become addicted to the Internet per se , but to the activities they engage in on the Internet, such as gaming [ 50 ] or SNS use [ 3 ]. With the advent and ubiquity of mobile technologies, this supposition is more pertinent than ever. Using social networking sites is a particularly popular activity on smartphones, with around 80% of social media used via mobile technologies [ 83 ]. For instance, approximately 75% of Facebook users access the SNS via their mobile phones [ 84 ]. Therefore, it can be suggested that smartphone addiction may be part of SNS addiction. Previous research [ 73 ] supported this supposition by specifically indicating that social networking is often engaged in via phones, which may contribute to its addictive potential. Accordingly, it is necessary to move towards nosological precision, for the benefit of both individuals seeking help in professional settings, as well as research that will aid developing effective treatment approaches for those in need.

2.8. Nomophobia May Be Part of SNS Addiction

Related to both FOMO and mobile phone addiction is the construct of nomophobia. Nomophobia has been defined as “no mobile phone phobia”, i.e., the fear of being without one’s mobile phone [ 85 ]. Researchers have called for nomophobia to be included in the DSM-5, and the following criteria have been outlined to contribute to this problem constellation: regular and time-consuming use, feelings of anxiety when the phone is not available, “ringxiety” (i.e., repeatedly checking one’s phone for messages, sometimes leading to phantom ring tones), constant availability, preference for mobile communication over face to face communication, and financial problems as a consequence of use [ 85 ]. Nomophobia is inherently related to a fear of not being able to engage in social connections, and a preference for online social interaction (which is the key usage motivation for SNSs [ 3 ]), and has been linked to problematic Internet use and negative consequences of technology use [ 86 ], further pointing to a strong association between nomophobia and SNS addiction symptoms.

Using mobile phones is understood as leading to alterations in everyday life habits and perceptions of reality, which can be associated with negative outcomes, such as impaired social interactions, social isolation, as well as both somatic and mental health problems, including anxiety, depression, and stress [ 85 , 87 ]. Accordingly, nomophobia can lead to using the mobile phone in an impulsive way [ 85 ], and may thus be a contributing factor to SNS addiction as it can facilitate and enhance the repeated use of social networking sites, forming habits that may increase the general vulnerability for the experience of addiction-related symptoms as a consequence of problematic SNS use.

2.9. There Are Sociodemographic Differences in SNS Addiction

Research suggests there are sociodemographic differences among those addicted to social networking. In terms of gender, psychotherapists treating technology-use related addictions suggest SNS addiction may be more common in female rather than male patients, and describe this difference based on usage motivations:

(…) girls don’t play role-playing games primarily, but use social forums excessively, in order to experience social interaction with other girls and above all to feel understood in their very individual problem constellations, very different from boys, who want to experience narcissistic gratification via games. This means the girls want direct interaction. They want to feel understood. They want to be able to express themselves. (…) we’re getting girls with clinical pictures that are so pronounced that we have to admit them into inpatient treatment. (…) we have to develop strategies to specifically target girls much better because there appears a huge gap. Epidemiologically, they are a very important group, but we’re not getting them into consultation and treatment. [ 34 ]

This quote highlights two important findings. First, in the age group of 14–16 years, girls appear to show a higher prevalence of addictions to the Internet and SNSs, as found in a representative German sample [ 88 ], and second, teenage girls may be underrepresented in clinical samples. Moreover, another study on a representative sample demonstrated that the distribution of addiction criteria varies between genders and that extraversion is a personality trait differentiating between intensive and addictive use [ 89 ].

Cross-sectional research is less conclusive as regards the contribution of gender as a risk factor for SNS addiction. A higher prevalence of Facebook addiction was found in a sample of 423 females in Norway using the Facebook Addiction Scale [ 58 ]. Among Turkish teacher candidates, the trend was reversed, suggesting males were significantly more likely to be addicted to using Facebook [ 90 ] as assessed via an adapted version of Young’s Internet Addiction Test [ 79 ].

In other studies, no relationship between gender and addiction was found. For instance, using a version of Young’s Internet Addiction Test modified for SNS addiction in 277 young Chinese smartphone users, gender did not predict SNS addiction [ 91 ]. Similarly, another study assessing SNS dependence in 194 SNS users did not find a relationship between gender and SNS dependence [ 51 ]. In a study of 447 university students in Turkey, Facebook addiction was assessed using the Facebook Addiction Scale, but did not find a predictive relationship between gender and Facebook addiction [ 62 ].

Furthermore, the relationships between gender and SNS addiction may be further complicated by other variables. For instance, recent research by Oberst et al. [ 74 ] found that only for females, anxiety and depression symptoms significantly predicted negative consequences of SNS use. The researchers explained this difference by suggesting that anxiety and depression experience in girls may result in higher SNS usage, implicating cyclical relationships in that psychopathological symptom experience may exacerbate negative consequences due to SNS use, which may then negatively impact upon perceived anxiety and depression symptoms.

In terms of age, studies indicate that younger individuals may be more likely to develop problems as a consequence of their excessive engagement with online social networking sites [ 92 ]. Moreover, research suggests perceptions as to the extent of possible addiction appear to differ across generations. A recent study by [ 72 ] found that parents view their adolescents’ online communication as more addictive than the adolescents themselves perceive it to be. This suggests that younger generations significantly differ from older generations in how they use technology, what place it has in their lives, and how problematic they may experience their behaviors to be. It also suggests that external accounts (such as those from parents in the case of children and adolescents) may be useful for clinicians and researchers in assessing the extent of a possible problem as adolescents may not be aware of the potential negative consequences that may arise as a result of their excessive online communication use. Interestingly, research also found that mothers are more likely to view their adolescents’ behavior as potentially more addictive relative to fathers, whose perception tended to be that of online communication use being less of a problem [ 72 ]. Taken together, although there appear differences in SNS addiction with regards to sociodemographic characteristics of the samples studied, such as gender, future research is required in order to clearly indicate where these differences lie specifically, given that much of current research appears somewhat inconclusive.

2.10. There Are Methodological Problems with Research to Date

Given that the research field is relatively young, studies investigating social networking site addiction unsurprisingly suffer from a number of methodological problems. Currently, there are few estimations of the prevalence of social networking addiction with most studies comprising small and unrepresentative samples [ 3 ]. As far as the authors are aware, only one study (in Hungary) has used a nationally representative sample. The study by Bányai and colleagues [ 93 ] reported that 4.5% of 5961 adolescents (mean age 16 years old) were categorized as ‘at-risk’ of social networking addiction using the Bergen Social Media Addiction Scale. However, most studies investigating social networking addiction use various assessment tools, different diagnostic criteria as well as varying cut-off points, making generalizations and study cross-comparisons difficult [ 53 ].

Studies have made use of several different psychometric scales and six of these are briefly described below. The Addictive Tendencies Scale (ATS) [ 94 ] is based on addiction theory and uses three items, salience, loss of control, and withdrawal, whilst viewing SNS addiction as dimensional construct. The Bergen Facebook Addiction Scale (BFAS) [ 58 ] is based on Griffiths’ [ 45 ] addiction components, using a polythetic scoring method (scoring 3 out of 4 on each criterion on a minimum of four of the six criteria) and has been shown to have good psychometric properties. The Bergen Social Media Addiction Scale is similar to the BFAS in that ‘ Facebook ’ is replaced with ‘Social Media’ [ 95 ]. The E-Communication Addiction Scale [ 72 ] includes 22 questions with four subscales scored on a five-point Likert scale—addressing issues such as lack of self-control (cognitive), e-communication use in extraordinary places, worries, and control difficulty (behavioral)—and it has been found to have a high internal consistency, measuring e-communication addiction across different severity levels, ranging from very low to very high.

The Facebook Dependence Questionnaire (FDQ) [ 96 ] uses eight items based on the Internet Addiction Scale [ 97 ], with the endorsement of five out of eight criteria signifying addiction to using Facebook . The Social Networking Addiction Scale (SNWAS) [ 51 ] is a five-item scale which uses Charlton and Danforth’s engagement vs. addiction questionnaire [ 98 , 99 ] as a basis, viewing SNS addiction as a dimensional construct. This is by no means an exhaustive list, but those assessment tools highlighted here simply demonstrate that the current social networking addiction scales are based on different theoretical frameworks and use various cut-offs, and this precludes researchers from making cross-study comparisons, and severely limits the reliability of current SNS epidemiological addiction research.

Taken together, the use of different conceptualizations, assessment instruments, and cut-off points decreases the reliability of prevalence estimates because it hampers comparisons across studies, and it also questions the construct validity of SNS addiction. Accordingly, researchers are advised to develop appropriate criteria that are clinically sensitive to identify individuals who present with SNS addiction specifically, whilst clinicians will benefit from a reliable and valid diagnosis in terms of treatment development and delivery.

3. Discussion

In this paper, lessons learned from the recent empirical literature on social networking and addiction have been presented, following on from earlier work [ 3 ] when research investigating SNS addiction was in its infancy. The research presented suggests SNSs have become a way of being, with millions of people around the world regularly accessing SNSs using a variety of devices, including technologies on the go (i.e., tablets, smartphones), which appear to be particularly popular for using SNSs. The activity of social networking itself appears to be specifically eclectic and constantly changing, ranging from using traditional sites such as Facebook to more socially-based online gaming platforms and dating platforms, all allowing users to connect based on shared interests. Research has shown that there is a fine line between frequent non-problematic habitual use and problematic and possibly addictive use of SNSs, suggesting that users who experience symptoms and consequences traditionally associated with substance-related addictions (i.e., salience, mood modification, tolerance, withdrawal, relapse, and conflict) may be addicted to using SNSs. Research has also indicated that a fear of missing out (FOMO) may contribute to SNS addiction, because individuals who worry about being unable to connect to their networks may develop impulsive checking habits that over time may develop into an addiction. The same thing appears to hold true for mobile phone use and a fear of being without one’s mobile phone (i.e., nomophobia), which may be viewed as a medium that enables the engagement in SNSs (rather than being addictive per se ). Given that engaging in social networking is a key activity engaged in using mobile technologies, FOMO, nomophobia, and mobile phone addiction appear to be associated with SNS addiction, with possible implications for assessment and future research.

In addition to this, the lessons learned from current research suggest there are sociodemographic differences in SNS addiction. The lack of consistent findings regarding a relationship with gender may be due to different sampling techniques and various assessment instruments used, as well as the presence of extraneous variables that may contribute to the relationships found. All of these factors highlight possible methodological problems of current SNS addiction research (e.g., lack of cross-comparisons due to differences in sampling and classification, lack of control of confounding variables), which need to be addressed in future empirical research. In addition to this, research suggests younger generations may be more at risk for developing addictive symptoms as a consequence of their SNS use, whilst perceptions of SNS addiction appear to differ across generations. Younger individuals tend to view their SNS use as less problematic than their parents might, further contributing to the contention that SNS use has become a way of being and is contextual, which must be separated from the experience of actual psychopathological symptoms. The ultimate aim of research must be not to overpathologize everyday behaviors, but to carry out better quality research as this will help facilitate treatment efforts in order to provide support for those who may need it.

Based on the 10 lessons learned from recent SNS addiction research, the following recommendations are provided. First, researchers are recommended to consider including an assessment of FOMO and/or nomophobia in SNS addiction screening instruments because both constructs appear related to SNS addiction. Second, it is recommended that social networking site use is measured across different technologies with which it can be accessed, including mobile and smartphones. It is of fundamental importance to study what kinds of activities are being engaged in online (social networking, gaming, etc.), rather than the medium through which these activities are engaged in (i.e., desktop computer, tablet, mobile/smartphone). Third, risk factors associated with problematic social networking need to be assessed longitudinally to provide a clearer indication of developmental etiology, and to allow for the design of targeted prevention approaches. Fourth, clinical samples need to be included in research in order to ensure the sensitivity and specificity of the screening instruments developed. Fifth, in terms of treatment, unlike treating substance-related addictions, the main treatment goal should be control rather than abstinence. Arguably, abstinence cannot realistically be achieved in the context of SNS addiction because the Internet and social networking have become integral elements of our lives [ 3 , 8 , 33 ]. Rather than discontinuing social networking completely, therapy should focus on establishing controlled SNS use and media awareness [ 53 ].

4. Conclusions

This paper has outlined ten lessons learned from recent empirical literature on online social networking and addiction. Based on the presented evidence, the way forward in the emerging research field of social networking addiction requires the establishment of consensual nosological precision, so that both researchers and clinical practitioners can work together and establish productive communication between the involved parties that enable reliable and valid assessments of SNS addiction and associated behaviors (e.g., problematic mobile phone use), and the development of targeted and specific treatment approaches to ameliorate the negative consequences of such disorders.

Acknowledgments

This work did not receive any funding.

Author Contributions

The first author wrote the first complete draft of the paper based on an idea by the second author. The authors then worked collaboratively and iteratively on subsequent drafts of the paper.

Conflicts of Interest

The authors declare no conflict of interest.

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Social Media Addiction

The Cause and Result of Growing Social Problems

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Accompanying the growth and increase in popularity of social media have been negative psychosocial and psychological effects associated with its excessive use. Research has shown a positive relationship between addiction-like behaviors associated with social media addiction (SMA) and psychological factors such as loneliness and low self-esteem, which demonstrate a congruency with recognized behavioral addictions. Adding to this congruency are the identified negative outcomes associated with SMA, which include difficulties in time perception, time management, maintaining interpersonal relationships, academic performance and increased prevalence of depression. According to the components model of addiction, the maladaptive behaviors/symptoms associated with problematic social media use (addiction) can be grouped into six dimensions, salience, tolerance, withdrawal, mood modification, conflict, and relapse. Studies have also identified several antecedents related to individual personality traits, fulfillment of psychological needs (relatedness, self-presentation, and social interaction), and perceived discrepancies between current and desired (or expected) interpersonal relationships (e.g., loneliness and low self-esteem). This chapter discusses the current understanding of SMA including its definition, measurement tools, and consequences. Further, it examines the underlying psychological and physiological explanations for addictive behaviors arising from social media use. The examination is based on a review of current theoretical understanding and the range of empirical studies, which examines the phenomena. Lastly, it highlights proposed social and policy approaches to alleviate the problem.

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Smith, T. (2023). Social Media Addiction. In: The Palgrave Handbook of Global Social Problems. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-68127-2_365-1

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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

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Acknowledgements

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

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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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Received : 26 January 2023

Accepted : 26 June 2023

Published : 13 July 2023

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

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