Judging a book by its cover: understanding the phenomenon of fake news propagation from an evolutionary psychology perspective
Bibliographic record
Abstract
With fake news now a serious concern facing researchers, practitioners, and policymakers alike, research is increasingly exploring the factors that lead to its proliferation. However, there is limited research on the role of temporal orientation. i.e., emphasis on time. This paper examines whether a future temporal orientation (FTO), defined as a relative emphasis on the future observed in fake news titles and content, is associated with fake news sharing. We bring arguments grounded in evolutionary psychology to understand the underlying rationale driving this phenomenon. Our analysis of a Twitter dataset comprising 465519 tweets suggests that FTO characterizes fake news and is positively associated with fake news sharing. Notably, fake news titles and the accompanying text differ in their FTO. Specifically, we show an inverted U-shaped relationship between fake news sharing and the difference in FTO between the title and accompanying text. As a practical implication of this analysis, efforts to limit the spread of fake news should pay more attention to how such news emphasizes the future.
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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 | 0.004 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".