Blatantly false news increases belief in news that is merely implausible
Bibliographic record
Abstract
What are the consequences of encountering blatant falsehoods and “fake news”? Here we show that exposure to a high prevalence of very implausible claims can increase belief in other, more ambiguous false claims, as they seem more believable in comparison. Participants in five preregistered experiments (N=5,476) were exposed to lower or higher rates of news headlines that seemed blatantly false, as well as some more plausible true and false headlines. Being exposed to a higher prevalence of extremely implausible headlines increased belief in unrelated headlines which were more ambiguous (or even plausible), regardless of whether they were true or false. The effect persisted for headlines describing hypothetical events, as well as actual true and false news headlines. It occurred whether people actively evaluated the headlines or read them passively, among liberals and conservatives, and among those high or low in cognitive reflection. We observed this effect in environments where the plausibility of a claim was a reliable and useful cue to whether it was true or false, and in environments where plausibility and truth were unrelated. We argue that a high prevalence of blatantly implausible claims lowers the threshold of plausibility for other claims to seem believable. Such relative comparisons are a hallmark of the brain’s tendency towards efficient computations in perception and judgment. Even when consumers can reliably identify and disregard blatantly false news content, encountering such content may make subtler falsehoods more likely to be believed.
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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 | 0.017 |
| 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) | 0.008 | 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 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".