Social Media and Self-Esteem

The Impact of Social Media Use on Sleep and Mental Health in Youth: a Scoping Review

PURPOSE OF REVIEW

Social media use (SMU) and other internet-based technologies are ubiquitous in today's interconnected society, with young people being among the commonest users. Previous literature tends to support that SMU is associated with poor sleep and mental health issues in youth, despite some conflicting findings. In this scoping review, we summarized relevant studies published within the past 3 years, highlighted the impacts of SMU on sleep and mental health in youth, while also examined the possible underlying mechanisms involved. Future direction and intervention on rational use of SMU was discussed.

RECENT FINDINGS

Both cross-sectional and longitudinal cohort studies demonstrated the negative impacts of SMU on sleep and mental health, with preliminary evidence indicating potential benefits especially during the COVID period at which social restriction was common. However, the limited longitudinal research has hindered the establishment of directionality and causality in the association among SMU, sleep, and mental health. Recent studies have made advances with a more comprehensive understanding of the impact of SMU on sleep and mental health in youth, which is of public health importance and will contribute to improving sleep and mental health outcomes while promoting rational and beneficial SMU. Future research should include the implementation of cohort studies with representative samples to investigate the directionality and causality of the complex relationships among SMU, sleep, and mental health; the use of validated questionnaires and objective measurements; and the design of randomized controlled interventional trials to reduce overall and problematic SMU that will ultimately enhance sleep and mental health outcomes in youth.

Relationship Between Self-Esteem and Problematic Social Media Use Amongst Chinese College Students: A Longitudinal Study

Purpose

Numerous cross-sectional investigations have delved into the association between the self-esteem of college students and problematic social media use (PSMU). Nevertheless, the debated causal sequence between the two factors persists, and their unclear developmental connection over time prevails.

Methods

We used a questionnaire method to follow 321 students in Chinese college over a period of 18 months and three times, of which 137 are male and 184 are female, with an average age of 18.42 years (SD = 0.78) at time 1. Descriptive statistics were used to present the general characteristics of the study participants. Moreover, the latent growth model was used to explore the developmental trajectory of self-esteem and PSMU and to explore the interplay between the two factors from a developmental perspective. On this basis, a cross-lagged model was used to verify the causal relationship between self-esteem and PSMU.

Results

In China, (a) the self-esteem levels of college students continuously decline during their academic years, whilst PSMU levels steadily increase. (b) The self-esteem of Chinese college students significantly negatively predicts PSMU. (c) The initial level of self-esteem amongst Chinese college students can significantly negatively predict the initial level of PSMU. Additionally, the rate of decline in self-esteem amongst Chinese college students can negatively predict the rate of increase in PSMU.

Conclusion

Obtained results serve as a valuable resource for researchers and educators, enabling strategic intervention in addressing issues related to PSMU amongst college students from a developmental perspective.

Heavy social media use and posting regret are associated with lower self-esteem among middle and high school students

OBJECTIVE

Social media use has become ubiquitous among schoolchildren. This study examined the association of social media use and posting regret with self-esteem among middle and high school students, and tested whether school type (i.e., middle vs. high school) could moderate these associations.

METHODS

Data on 6944 schoolchildren (mean age: 15.2 ± 1.8 years) were derived from the 2019 cycle of the Ontario Student Drug Use and Health Survey, a biennial repeated cross-sectional survey of students in grades 7 through 12 across Ontario, Canada. Students self-reported time spent using social media, regret of posting on social media, and their self-esteem. Ordinal logistic regression analyses were adjusted for age, sex, subjective socioeconomic status, ethnoracial background, and body mass index z-score.

RESULTS

The proportional odds model showed that middle (OR: 2.36; 95% CI: 1.65-3.36) and high school (OR: 1.72; 95% CI: 1.44-2.06) students with daily social media use of 5 h or more have higher odds of lower self-esteem across the categories of self-esteem. However, daily use of 3 to 4 h was associated with lower self-esteem among middle school students (OR: 1.52; 95% CI: 1.14-2.04), but not among their high school counterparts (OR: 1.13; 95% CI: 0.94-1.35). Posting regret was associated with lower self-esteem in high school students, but not in middle school students.

CONCLUSION

Results suggest that heavy social media use and posting regret are associated with lower self-esteem among adolescents, and that younger students could be more vulnerable than their older counterparts.

How social media usage affects psychological and subjective well-being: testing a moderated mediation model

BACKGROUND

A growing body of literature demonstrates that social media usage has witnessed a rapid increase in higher education and is almost ubiquitous among young people. The underlying mechanisms as to how social media usage by university students affects their well-being are unclear. Moreover, current research has produced conflicting evidence concerning the potential effects of social media on individuals' overall well-being with some reporting negative outcomes while others revealing beneficial results.

METHODS

To address the research gap, the present research made an attempt to investigate the crucial role of social media in affecting students' psychological (PWB) and subjective well-being (SWB) by testing the mediating role of self-esteem and online social support and the moderation effect of cyberbullying. The data in the study were obtained from a sample of 1,004 college students (483 females and 521 males, Mage = 23.78, SD = 4.06) enrolled at 135 Chinese universities. AMOS 26.0 and SPSS 26.0 as well as the Process macro were utilized for analyzing data and testing the moderated mediation model.

RESULTS

Findings revealed that social media usage by university students was positively associated with their PWB and SWB through self-esteem and online social support, and cyberbullying played a moderating role in the first phase of the mediation process such that the indirect associations were weak with cyberbullying reaching high levels.

CONCLUSION

These findings highlight the importance of discerning the mechanisms moderating the mediated paths linking social media usage by young adults to their PWB and SWB. The results also underline the importance of implementing measures and interventions to alleviate the detrimental impacts of cyberbullying on young adults' PWB and SWB.

Social Drivers and Algorithmic Mechanisms on Digital Media

On digital media, algorithms that process data and recommend content have become ubiquitous. Their fast and barely regulated adoption has raised concerns about their role in well-being both at the individual and collective levels. Algorithmic mechanisms on digital media are powered by social drivers, creating a feedback loop that complicates research to disentangle the role of algorithms and already existing social phenomena. Our brief overview of the current evidence on how algorithms affect well-being, misinformation, and polarization suggests that the role of algorithms in these phenomena is far from straightforward and that substantial further empirical research is needed. Existing evidence suggests that algorithms mostly reinforce existing social drivers, a finding that stresses the importance of reflecting on algorithms in the larger societal context that encompasses individualism, populist politics, and climate change. We present concrete ideas and research questions to improve algorithms on digital platforms and to investigate their role in current problems and potential solutions. Finally, we discuss how the current shift from social media to more algorithmically curated media brings both risks and opportunities if algorithms are designed for individual and societal flourishing rather than short-term profit.

Online health community for change: Analysis of self-disclosure and social networks of users with depression

Background

A key research question with theoretical and practical implications is to investigate the various conditions by which social network sites (SNS) may either enhance or interfere with mental well-being, given the omnipresence of SNS and their dual effects on well-being.

Method/process

We study SNS’ effects on well-being by accounting for users’ personal (i.e., self-disclosure) and situational (i.e., social networks) attributes, using a mixed design of content analysis and social network analysis.

Result/conclusion

We compare users’ within-person changes in self-disclosure and social networks in two phases (over half a year), drawing on Weibo Depression SuperTalk, an online community for depression, and find: ① Several network attributes strengthen social support, including network connectivity, global efficiency, degree centralization, hubs of communities, and reciprocal interactions. ② Users’ self-disclosure attributes reflect positive changes in mental well-being and increased attachment to the community. ③ Correlations exist between users’ topological and self-disclosure attributes. ④ A Poisson regression model extracts self-disclosure attributes that may affect users’ received social support, including the writing length, number of active days, informal words, adverbs, negative emotion words, biological process words, and first-person singular forms.

Innovation

We combine social network analysis with content analysis, highlighting the need to understand SNS’ effects on well-being by accounting for users’ self-disclosure (content) and communication partners (social networks).

Implication/contribution

Authentic user data helps to avoid recall bias commonly found in self-reported data. A longitudinal within-person analysis of SNS’ effects on well-being is helpful for policymakers in public health intervention, community managers for group organizations, and users in online community engagement.

Do evaluation and self-identification relate to self-reported and actual social media use?

Background and aims

Social media use can sometimes become excessive and damaging. To deal with this issue, scholars and practitioners have called for the development of measures that predict social media use. The current studies test the utility of evaluation and self-identification measures for predicting social media use.

Method

Study 1 examined the relation between evaluation (n = 58) and self-identification (n = 56) measures on the one hand and several self-report measures of social media use on the other hand. Study 2 examined whether the main results of Study 1 could be replicated and whether evaluation (n = 68) and self-identification (n = 48) also relate to actual social media use. We probed evaluation and self-identification using implicit and explicit measures.

Results

Explicit evaluation and self-identification measures significantly correlated with several of the self-report measures of social media. Explicit evaluation also significantly correlated with several indices of actual social media use. Implicit measures did not relate to social media use.

Discussion and conclusions

The current results suggest that researchers and practitioners could benefit from using explicit evaluation and self-identification measures when predicting social media use, especially an evaluation measure since this measure also seems to relate to actual social media use. Study 2 was one of the first to test the ecological validity of social media use measures. Although implicit measures could provide benefits for predicting social media use, the current studies did not show evidence for their predictive utility.