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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 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".