When Every Day is a High School Reunion: Social Media Comparisons and Self-Esteem
Bibliographic record
Abstract
Although past research has shown that social comparisons made through social media contribute to negative outcomes, little is known about the nature of these comparisons (domains, direction, and extremity), variables that determine comparison outcomes (post valence, perceiver’s self-esteem), and how these comparisons differ from those made in other contexts (e.g., text messages, face-to-face interactions). In four studies (N=798), we provide the first comprehensive analysis of how individuals make and respond to social comparisons on social media, using comparisons made in real-time while browsing news feeds (Study 1), experimenter-generated comparisons (Study 2), and comparisons made on social media vs. in other contexts (Studies 3-4). More frequent and more extreme upward comparisons resulted in immediate declines in self-evaluations as well as cumulative negative effects on individuals’ state self-esteem, mood, and life satisfaction after a social media browsing session. Moreover, downward and lateral comparisons occurred less frequently and did little to mitigate upward comparisons’ negative effects. Furthermore, low self-esteem individuals were particularly vulnerable to making more frequent and more extreme upward comparisons on social media, which in turn threatened their already-lower self-evaluations. Finally, social media comparisons resulted in greater declines in self-evaluation than those made in other contexts. Together, these studies provide the first insights into the cumulative impact of multiple comparisons, clarify the role of self-esteem in online comparison processes, and demonstrate how the characteristics and impact of comparisons on social media differ from those made in other contexts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".