Focusing on fake news’ contents: The association between ingroup identification, perceived outgroup threat, analytical‐intuitive thinking and detecting fake news
Bibliographic record
Abstract
Abstract This study aims to reveal the fake news content in the context of the social identity approach and to examine the mediating role of perceived outgroup on the association between ingroup identification and detecting fake news blaming ingroup, outgroup, or fictional groups. Study 1 found that fake news could be gathered under six themes: contacted‐outgroup blaming, represented‐outgroup blaming, outgroup derogation, outgroup appreciation, ingroup glorification, and phantom‐mastermind blaming. In preregistered Study 2 with representative non‐weird participants (N = 216), we examined the mediating role of perceived outgroup threat on the association between ingroup identification and detecting fake news revealed in Study 1. Perceived outgroup threat was only mediating for detecting outgroup‐blaming fake news when intuitive and analytical thinking styles were controlled. Detecting ingroup‐blaming fake news was associated with ingroup identification. Analytical thinking predicted only detecting phantom‐mastermind‐blaming fake news. Findings demonstrated that the contents of fake news play a vital role in detecting them, and variables pointing to content (i.e., ingroup identification for ingroup‐blaming fake news, and perceived outgroup threat for outgroup‐blaming fake news) are predictive for detecting 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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".