Fake news, fast and slow: Deliberation reduces belief in false (but not true) news headlines
Bibliographic record
Abstract
What role does deliberation play in susceptibility to political misinformation and “fake news”? The “Motivated System 2 Reasoning” account posits that deliberation causes people to fall for fake news because reasoning facilitates identity-protective cognition and is therefore used to rationalize content that is consistent with one’s political ideology. The classical account of reasoning instead posits that people ineffectively discern between true and false news headlines when they fail to deliberate (and instead rely on intuition). To distinguish between these competing accounts, we investigated the causal effect of reasoning on media truth discernment using a two-response paradigm. Participants (N= 1635 MTurkers) were presented with a series of headlines. For each, they were first asked to give an initial, intuitive response under time pressure and concurrent working memory load. They were then given an opportunity to re-think their response with no constraints, thereby permitting more deliberation. We also compared these responses to a (deliberative) one-response baseline condition where participants made a single choice with no constraints. Consistent with the classical account, we found that deliberation corrected intuitive mistakes: subjects believed false headlines (but not true headlines) more in initial responses than in either final responses or the unconstrained 1-response baseline. In contrast – and inconsistent with the Motivated System 2 Reasoning account – we found that political polarization was equivalent across responses. Our data suggest that, in the context of fake news, deliberation facilitates accurate belief formation and not partisan bias.
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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 | 0.002 | 0.016 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".