Beyond “fake news”: Analytic thinking and the detection of false and hyperpartisan news headlines
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Why is misleading partisan content believed and shared? An influential account posits that political partisanship pervasively biases reasoning, such that engaging in analytic thinking exacerbates motivated reasoning and, in turn, the acceptance of hyperpartisan content. Alternatively, it may be that susceptibility to hyperpartisan content is explained by a lack of reasoning. Across two studies using different participant pools (total N = 1,973 Americans), we had participants assess true, false, and hyperpartisan news headlines taken from social media. We found no evidence that analytic thinking was associated with judging politically consistent hyperpartisan or false headlines to be accurate and unbiased. Instead, analytic thinking was, in most cases, associated with an increased tendency to distinguish true headlines from both false and hyperpartisan headlines (and was never associated with decreased discernment). These results suggest that reasoning typically helps people differentiate between low and high quality political news, rather than facilitate belief in misleading content. Because social media play an important role in the dissemination of misinformation, we also investigated willingness to share headlines on social media. We found a similar pattern whereby analytic thinking was not generally associated with increased willingness to share hyperpartisan or false headlines. Together, these results suggest a positive role for reasoning in resisting misinformation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it