Fake News and True News Assessment: The Persuasive Effect of Discursive Evidence in Judging Veracity
Bibliographic record
Abstract
Individuals are often unable to assess the veracity of news claims—especially on social media platforms. Recent research has suggested that interventions indicating normative signals, such as flagging false claims, are not always effective. We propose an approach in which users are provided with discursive evidence to consider in determining the veracity of claims rather than depending on normative true or false flags. We conducted a series of experiments to explore the effects of different forms of discursive evidence on individual judgments of the veracity of news claims. We found that providing such evidence can significantly improve individuals’ judgment of both true and false news claims—with certain caveats. Providing discursive evidence with high evidence strength leads to a general increase in veracity judgment. Discursive evidence containing items with lower evidence strength may shift believability— thus improving judgments for either true or false claims but degrading them for the other. We also identify important asymmetries between true and false claims, finding that the effect of some evidence may be improved if people are in a more critical mindset—for example, by priming them to think about the concept of truth and lies. Taken together, these results extend knowledge on the problem of fake news and may suggest effective approaches to address the problem without diminishing attention to true news.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".