Are active and passive social media use related to mental health, wellbeing, and social support outcomes? A meta-analysis of 141 studies
Bibliographic record
Abstract
Abstract The relationships between active (e.g., creating content) and passive (e.g., scrolling) social media (SM) use (SMU) and mental health, wellbeing, and social support outcomes have received significant attention, yet findings have been mixed. We conducted a meta-analysis of 141 studies (N ≈ 145,000) containing 897 effect sizes (ESs) between active and passive SMU and 13 outcomes. Most ESs were negligible (|r| < .10), with the exception of between-person associations for active SMU and greater online support (r = .34), wellbeing (r = .15), positive affect (r = .11), and symptoms of anxiety (r = .12), and passive SMU and greater online support (r = .15). Moderator analyses revealed that passive use was associated with worse emotional outcomes in general SM contexts, but not in the context of SM groups. User age also emerged as an important contextual factor. Implications for future research, theory development, and healthy SMU are discussed.
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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 | 0.021 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".