Social media platforms increasingly disclose when news is fake or misleading. Is it helpful?
Bibliographic record
Abstract
Social media platforms have increasingly adopted fact-checking labels to combat the spread of misinformation. However, existing research has focused primarily on whether these interventions reduce belief in false claims, overlooking a potentially more harmful effect: the activation of prejudice against targeted groups. This study investigates whether fact-checking labels prevent prejudice activation when users encounter fake news targeting Muslims. Using an experimental design with three conditions: undisclosed fake news, disclosed (fact-checked) fake news, and a control group, I surveyed over 1400 participants across two studies to measure attitudes toward Muslims after exposure to fabricated anti-Muslim content. Results were inconsistent across my surveys: while the first study showed that disclosure significantly reduced negative attitudes compared to undisclosed fake news (p=0.009), the second study failed to replicate this effect (p=0.55). Notably, both disclosed and undisclosed groups consistently clustered together and separately from the control group, suggesting that mere exposure to inflammatory content may influence attitudes regardless of fact-checking. These findings indicate that current platform interventions may be insufficient to prevent prejudice activation, even when they successfully correct false beliefs. The study suggests a societal need for alternative approaches beyond labeling to protect marginalized communities from the harmful effects of orchestrated disinformation campaigns.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".