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.
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 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 | 0.001 | 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) | 0.001 | 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".