Examining the Relationship Between Social Media Use and Addiction and Body Dysmorphic Disorder Symptoms: A Meta-Analytic Review
Bibliographic record
Abstract
Relative to body image, comparatively less research has examined the relationship between social media (SM) use and body dysmorphic disorder (BDD). The current meta-analysis examined the cross-sectional relationship between BDD symptoms and SM use/addiction. Of the 1619 studies initially identified, 16 met inclusion criteria. Results revealed a moderate aggregate correlation between SM addiction and BDD symptoms (r = .38, p < .001; n = 9), which was significantly stronger than the weak aggregate correlation revealed between frequency of SM use and BDD symptoms (r = .18, p < .001; n = 10). Of the moderators examined, BDD measure, sex/gender, and SM platform were found to significantly moderate the relationship between BDD and SM addiction. Specifically, studies that measured BDD via the Body Image Concern Inventory, that had a lower percentage of female participants, and that focused on general SM use rather than Instagram use, produced stronger correlations. However, the latter two correlations were only significant when an outlier was removed. Although our results suggest a relationship exists between BDD and SM use/addiction, more experimental and longitudinal research is needed before the direction or presence of causality between these two variables can be determined.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.015 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".