Accuracy prompts protect professional content moderators from the illusory truth effect
Bibliographic record
Abstract
Content moderators review problematic content for technology companies. One concern about this critical job 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 field experiment with a global content moderation company (N = 199), we found that exposure to false claims while working as moderators did indeed increase subsequent belief among (mostly Indian and Philippine) employees. We then tested an intervention to mitigate this effect: inducing an accuracy mindset. In both general population samples (N_India = 997; N_Philippines = 1184) and a second field experiment with professional content moderators (N = 239), we replicate the illusory truth effect in the control condition, and find that inducing participants to consider accuracy when first exposed to the claims eliminates any effect of exposure on belief in falsehoods. These results show that both the illusory truth effect and the protective power of an accuracy mindset generalize to non-Western populations and professional content moderators. These results highlight the importance of accuracy mindset interventions for ensuring a healthy internet for everyone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".