Well-Being Associations with Daily Activities on Instagram and Moderation by Beliefs About Social Media Addiction
Bibliographic record
Abstract
Debates about the effects of social media on well-being are ongoing, fueled in part by mixed and weak associations found in published research. Researchers have relied heavily on measuring time spent on social media (i.e., “screen time”) and self-reports that fail to capture the detail of young people’s day-to-day activities and interactions on branded apps like Instagram. Further, inconsistent findings may be due in part to beliefs young people hold about social media’s role in harming well-being. This study proposes to obtain direct and objective application data from Instagram to test whether specific activities and interactions (posting, liking, commenting, browsing) are related to daily well-being. We will also test whether these associations are shaped by young people’s pre-existing beliefs about the addictive properties of social media. Young adults aged 18 to 25 who use Instagram daily will be recruited online. Participants will complete 15 daily surveys distributed semi-randomly during a 30-day period during which time Instagram application data will be gathered passively, using Meta’s Instagram account authorization flow enabled as part of Meta and the Center for Open Science’s Instagram Data Access Pilot (Meta Platforms, Inc., 2025). Multilevel linear models will test confirmatory and exploratory research questions. Results and discussion will follow with the Stage 2 report.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".