Scaling up fact-checking using the wisdom of crowds
Bibliographic record
Abstract
Misinformation on social media has become a major focus of research and concern in recent years. Perhaps the most prominent approach to combating misinformation is the use of professional fact-checkers. This approach, however, is not scalable: Professional fact-checkers cannot possibly keep up with the volume of misinformation produced every day. Furthermore, not everyone trusts fact-checkers, with some arguing that they have a liberal bias. Here, we explore a potential solution to both of these problems: leveraging the “wisdom of crowds'' to identify misinformation at scale using politically-balanced groups of laypeople. Using a set of 207 news articles flagged for fact-checking by an internal Facebook algorithm, we compare the accuracy ratings given by (i) three professional fact-checkers after researching each article and (ii) 1,128 Americans from Amazon Mechanical Turk after simply reading the headline and lede sentence. We find that the average rating of a small politically-balanced crowd of laypeople is as correlated with the average fact-checker rating as the fact-checkers’ ratings are correlated with each other. Furthermore, the layperson ratings can predict whether the majority of fact-checkers rated a headline as “true” with high accuracy, particularly for headlines where all three fact-checkers agree. We also find that layperson cognitive reflection, political knowledge, and Democratic Party preference are positively related to agreement with fact-checker ratings; and that informing laypeople of each headline’s publisher leads to a small increase in agreement with fact-checkers. Our results indicate that crowdsourcing is a promising approach for helping to identify misinformation at scale.
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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 | 0.047 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".