Understanding the relationship between social media use and depression: a review of the literature
Bibliographic record
Abstract
Purpose This paper analyzes the existing body of work on the relationship between depression and social media use in the information system field, including the impact of social media use on depression, the effect of depression on social media use and the association and interaction between depression and social media use. Design/methodology/approach Using the systematic review method, this study selected the Web of Science, Emerald, Science Direct, JSTOR, Wiley Online Library and Taylor and Francis Online as search databases and ended up with 29 papers that met all the authors' requirements. Findings This study identified five possible reasons for the inconsistencies between the findings of the selected studies. First, uses and gratifications theory has different influence mechanisms in evaluating the relationship between social media use and depression. Second, gender can moderate the impact of social media use on depression. Third, age moderates the association between social media use and depression. Fourth, for adolescents, the time spent on social media has a critical effect on their depression. Fifth, negative personality traits (e.g. rumination, envy, etc.) can play a significant role in mediating the relationship between passive social media use and depression. Originality/value This study conducted an evaluation of the relationship between depression and social media use. First, the authors summarized the research framework and main body of work covering the relationship between depression and social media use. Second, the authors proposed possible explanations for the inconsistencies between the findings. Third, the authors discussed and explained the possible influence mechanisms of the existing results. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-04-2021-0211 .
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".