Actively open-minded thinking is key to combating fake news: A multimethod study
Bibliographic record
Abstract
The fake news phenomenon has exposed the vulnerability of individuals and societies to information manipulation in social media. We conducted two studies to understand why people believe in fake news and propose a simple IT intervention method that can help in detecting disinformation. In Study 1, we designed a laboratory experiment using behavioral and neurophysiological tools to test two competing theories in the disinformation literature. Both behavioral and neurophysiological evidence support the classical reasoning account hypotheses and reject the motivated reasoning predictions, suggesting that the lack of actively open-minded thinking (AOT) is linked to the belief in fake news. An intervention method was designed (i.e., performance feedback) that reduces individuals’ overconfidence in their ability to detect fake news and encourages more analytical thinking. In Study 2, we conducted an online survey presenting participants with their performance feedback halfway through the survey. The results show that the intervention increased participants’ performance by 14%. Our study contributes to the research on fake news by providing behavioral and neurophysiological evidence in support of the classical reasoning account. It also offers a simple and practical method that increases users’ ability to detect fake news.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".