The Psychological Appeal of Fake-News Attributions
Bibliographic record
Abstract
The term fake news is increasingly used to discredit information from reputable news organizations. We tested the possibility that fake-news claims are appealing because they satisfy the need to see the world as structured. Believing that news organizations are involved in an orchestrated disinformation campaign implies a more orderly world than believing that the news is prone to random errors. Across six studies ( N > 2,800), individuals with dispositionally high or situationally increased need for structure were more likely to attribute contested news stories to intentional deception than to journalistic incompetence. The effect persisted for stories that were ideologically consistent and ideologically inconsistent and after analyses controlled for strength of political identification. Political orientation showed a moderating effect; specifically, the link between need for structure and belief in intentional deception was stronger for Republican participants than for Democratic participants. This work helps to identify when, why, and for whom fake-news claims are persuasive.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".