The ephemeral effects of fact-checks on COVID-19 misperceptions: Evidence from the United States, Great Britain, and Canada
Bibliographic record
Abstract
Widespread misperceptions about COVID-19 and the novel coronavirus threaten to exacerbate the severity of the pandemic. We conducted preregistered survey experiments in the United States, Great Britain, and Canada examining the effectiveness of fact-checks that seek to correct these false or unsupported misperceptions. Across three countries with differing levels of political conflict over the COVID-19 response, we demonstrate that fact-checks reduce targeted misperceptions, especially among the groups who are most vulnerable to these claims, and have minimal spillover effects on the accuracy of other beliefs about COVID-19. However, the positive effects of fact-checks on the accuracy of respondents' beliefs fail to persist over time in panel data even after repeated exposure. These results suggest that fact-checks can successfully change the beliefs of the people who would benefit from them most but that their effects are disappointingly ephemeral.
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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 | 0.005 | 0.265 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.024 | 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".