Social Media Use and Well-Being: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Prior research has investigated the link between social media use (SMU) and negative well-being. However, the connection with positive well-being has not been extensively studied, leading to a situation where there are inconsistent and inconclusive findings. This study fills this gap by examining the correlation between excessive and problematic SMU and subjective as well as psychological well-being (PWB). We conducted a systematic search across databases such as PubMed, Scopus, and Web of Science, and gray literature sources such as Research Gate and ProQuest, yielding 51 relevant studies for meta-analysis, encompassing a sample size of 680,506 individuals. Employing the Newcastle–Ottawa Scale, we assessed study quality, whereas statistical analysis was executed using R Studio. Excessive SMU showed no significant association with subjective ( ES = 0.003, 95% confidence interval [ 95% CI ]: −0.08, 0.09; p = 0.94, I 2 = 95.8%, k =16) and PWB ( ES = 0.16, 95% CI : −0.15, 0.45; p = 0.26, I 2 = 98%, k = 7). Conversely, problematic SMU showed a negative correlation with subjective ( ES = −0.14 , 95% CI : −0.20, −0.09; p = 0.00, I 2 = 93.3%, k = 25) and PWB ( ES = −0.19 , 95% CI : −0.31, −0.06; p = 0.01, I 2 = 95%, k = 5), with two outliers removed. No publication bias was detected. Subgroup analysis highlighted effects of “sampling method” ( p < 0.05), “study quality” ( p < 0.05), “developmental status” ( p < 0.05), “forms of social media” ( p < 0.05), and “type of population” ( p < 0.01) on the estimated pooled effect sizes. Although univariate meta-regression showed the effects of “% of Internet users” ( p < 0.05) and “male%” ( p < 0.05), and multivariate meta-regression showed the combined effect of moderators only on the relationship between problematic SMU and subjective well-being.
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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 | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.010 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".