Examining Partisan Asymmetries in Fake News Sharing and the Efficacy of Accuracy Prompt Interventions
Bibliographic record
Abstract
The spread of misinformation has become a central concern in American politics. Recent studies of social media sharing suggest that Republicans are considerably more likely to share fake news than Democrats. However, such inferences are confounded by the far greater supply of right-leaning fake news—Republicans may indeed be more prone to sharing fake news, or they may simply be more exposed to it. This article disentangles these competing explanations by examining sharing intentions in a balanced information environment. Using a large national survey of YouGov respondents, we show that Republicans are indeed more prone to sharing ideologically concordant fake news than Democrats, but that this gap is not large enough to explain differences in sharing observed online. Encouragingly, however, we also find that accuracy prompt interventions that reduce the spread of fake news are equally effective across parties, suggesting that fake news sharing among Republicans is not an intractable problem.
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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 | 0.001 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".