A Positive Empathy Intervention to Improve Well-being on Instagram
Bibliographic record
Abstract
With more than half the global population on social media, there is a critical need to understand how to engage it in a way that improves rather than worsens user well-being. Here, we show that positive empathy is a promising tool. Participants who received brief positive empathy instructions before 10 minutes of browsing their own Instagram feed showed greater affective well-being (Studies 1-4) and life satisfaction (Study 4) at post-test relative to participants who were instructed to browse as usual. The positive empathy intervention showed an average effect size on well-being of about a quarter of a standard deviation (mean Cohen’s d = 0.25). We included unique active-control groups in each study. We found using positive empathy on social media was about as beneficial to well-being as watching a nature video (Study 1, N = 298) and was better than instructions to focus on positive content (Study 2, N = 302), empathize with all emotions (Study 3, N = 301) or reappraise one’s own emotions (Study 4, N = 426). We used structural equation modeling to demonstrate the effect of the intervention on subjective well-being is mediated by changes in positive emotion sharing, appreciative joy, and self-compassion. These experiences form a latent factor we term positive empathy. Our results show that a brief intervention successfully manipulates positive empathy on Instagram, which increases well-being.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".