The associations between social comparison on social media and young adults’ mental health
Bibliographic record
Abstract
Introduction Social networking sites (SNSs) have become an integral part of daily life, raising concerns about their potential impact on mental health. One key mechanism through which SNSs may affect wellbeing is social comparison. The present research aimed to examine the mediating role of social comparisons (upward and downward) in the relationship between SNSs use and self-esteem (global and physical). Methods Study 1 ( N = 139; female 51%; 73% White), conducted during the COVID-19 pandemic, tested whether perceived exposure to social comparisons mediated the relationship between Instagram use and self-esteem. Results Results revealed that upward comparisons mediated the association between Instagram use and lower global self-esteem, but no significant mediation was found for physical self-esteem. Study 2 ( N = 413; 58% female; 62% White), conducted post-pandemic, extended these findings by including two SNSs (Instagram and Facebook), the extremity of upward comparisons (how far superior the comparison target is perceived to be), social feedback (responses or evaluations from others) and measures of depressive symptoms. As expected, exposure to upward comparisons negatively mediated the relationship between SNSs use and self-esteem (both global and physical), and positively mediated the relationship between SNSs use and depressive symptoms. However, contrary to our hypothesis, frequent SNSs users engaged in less extreme upward comparisons, partially buffering the negative impact of extreme upward comparisons. Discussion Together, these findings highlight the crucial role of both exposure to and extremity of upward social comparisons in the complex relationship between SNSs use and mental health. These two factors contribute significantly though modestly to the effects of SNSs on self-esteem and depressive symptoms ( R 2 between 6 and 9%), underscoring the need for further research on individual and contextual variables that may mitigate their adverse psychological consequences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".