Displaying News Source Trustworthiness Ratings Reduces Sharing Intentions for False News Posts
Bibliographic record
Abstract
Professional fact-checking of individual news headlines is an effective way to fight misinformation, but it is not easily scalable, because it cannot keep pace with the massive speed at which news content gets posted on social media. Here we provide evidence for the effectiveness of ratings of news sources, instead of individual news articles. In a large pre-registered experiment with quota-sampled Americans, we find that participants are less likely to share false headlines (and more discerning of true versus false headlines) when 1-to-5 star trustworthiness ratings were applied to news headlines. This is true both when the ratings are generated by fact-checkers and by laypeople (although the effect is stronger using fact-checker ratings). We also observe a positive spillover effect: sharing discernment also increases for headlines whose source was not rated, likely because the presence of ratings on some headlines prompts users to reflect on source quality more generally. This study suggests that displaying information regarding the trustworthiness of news sources provides a scalable approach for reducing the spread of low-quality information.
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 | 0.005 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".