Can intergroup contact on social media improve intergroup relations? Developing and testing a longitudinal intergroup contact field intervention on social media.
Bibliographic record
Abstract
Intergroup contact may be the best known tool for reducing prejudice and improving intergroup relations. Yet, challenges inherent to studying and applying it hold the field back from answering basic questions about it definitively and undermine its applied readiness. We propose that using social media to study intergroup contact may help push contact research forward to applied readiness and help us to better understand intergroup contact itself. To do so, we present three studies totaling 4,621 observations from 646 participants and drawing on observations of 193,225 social media users that develop and test a social media-based intergroup contact intervention to reduce prejudice. We found that intergroup contact on social media was associated with less prejudice and more positive intergroup behavior cross-sectionally and longitudinally, but we did not find that manipulating the racial demographics of accounts posting to participants' real Twitter feeds had a causal effect on their intergroup attitudes or behaviors. These results suggest that although social media contexts may be fertile ground for studying and applying intergroup contact, we do not yet have evidence for an effect that is causal in nature. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".