Bidirectional Associations of Adolescents’ Momentary Social Media Use and Negative Emotions
Bibliographic record
Abstract
Abstract Public discourse and empirical studies have predominantly focused on the negative repercussions of social media on adolescents’ mental health. However, pervasive social media use is a relatively new phenomenon—its apparent harms have been widely accepted before sufficient longitudinal and experimental research has been conducted. The present study used an intensive longitudinal design (four assessments/day × 14 days; N = 154 12- to 15-year-olds (Mage = 13.47, SD = 0.58); N = 6,240 valid measurement occasions) to test the directionality of social media–negative emotion links in early adolescence, accounting for the type of social media usage (i.e., browsing vs. posting). The significance of effects depended on social media type: browsing predicted higher-than-usual negative emotions hours later, whereas no significant directional effects emerged for posting. The browsing effect was small but held after controlling for prior levels of negative emotions. It did not replicate concurrently, underscoring the importance of process-oriented designs with mental health symptoms tested shortly after passive social media usage. The results partially support the active-passive hypothesis, which singles out passively engaging with others’ curated social media content as most detrimental to mental health. Nonetheless, the small browsing effect and overall null-leaning pattern of effects imply that mediators and moderators are needed to further understand when using social media is problematic, beneficial, or neither.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".