The Longitudinal Association Between Social-Media Use and Depressive Symptoms Among Adolescents and Young Adults: An Empirical Reply to Twenge et al. (2018)
Bibliographic record
Abstract
Research by Twenge, Joiner, Rogers, and Martin has indicated that there may be an association between social-media use and depressive symptoms among adolescents. However, because of the cross-sectional nature of this work, the relationship among these variables over time remains unclear. Thus, in this longitudinal study we examined the associations between social-media use and depressive symptoms over time using two samples: 594 adolescents ( M age = 12.21) who were surveyed annually for 2 years, and 1,132 undergraduate students ( M age = 19.06) who were surveyed annually for 6 years. Results indicate that among both samples, social-media use did not predict depressive symptoms over time for males or females. However, greater depressive symptoms predicted more frequent social-media use only among adolescent girls. Thus, while it is often assumed that social-media use may lead to depressive symptoms, our results indicate that this assumption may be unwarranted.
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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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.019 | 0.022 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".