Focusing on fake news’ contents: The association between ingroup identification, perceived outgroup threat, analytical‐intuitive thinking and detecting fake news
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 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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