Accuracy prompts protect professional content moderators from the illusory truth effect
Bibliographic record
Abstract
Abstract Content moderators review problematic content for technology companies. One concern is that repeated exposure to false claims could cause moderators to come to believe the very claims they are supposed to moderate, via the “illusory truth effect.” In a first lab-in-field experiment (N = 199) with a global content moderation company, we found that exposure to false claims while working as moderators increased subsequent belief among (mostly Indian and Philippine) employees by 7.1%. We tested an intervention to mitigate this effect: inducing an accuracy mindset. In both general population samples (NIndia = 997; NPhilippines = 1,184) and a second lab-in-field experiment with professional moderators (N = 239), inducing participants to consider accuracy when first exposed to the claims eliminates the negative effects of exposure on belief in falsehoods. Our results show that the illusory truth effect and the protective power of an accuracy mindset generalize to non-Western populations and professional moderators.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".