Rumour type matters: The effect of different types of rumours on coping, subjective well‐being, and interpersonal trust during the COVID‐19 pandemic
Bibliographic record
Abstract
Rumours circulated quickly online and offline during the COVID-19 pandemic, but empirical research on the subject is limited. Combining qualitative (Study 1, content analysis was conducted on 2344 actual rumours extracted from a rumour-refuting website) and quantitative methods (Study 2, a three-wave study with 10-day intervals), the current study suggests that (1) rumours during the pandemic can be categorised into three types, that is, wish, dread, and aggression rumours, and (2) exposure to different types of rumours is associated with coping consequences, subjective well-being (comprising positive affect, negative affect, and life satisfaction), and interpersonal trust in different ways. Generally, wish rumours seem benign, while dread and aggression rumours are malicious. Specifically, wish rumours are believed to assist coping and to be positively associated with positive affect and interpersonal trust. In contrast, dread rumours are believed not to assist coping and to be marginally significantly and positively associated with negative affect and negatively associated with interpersonal trust. Meanwhile, aggression rumours are believed not to assist coping and are marginally significantly and positively associated with negative affect. All other relationships are nonsignificant. The results of the current study will help national governments and international agencies design and evaluate rumour control strategies and policies.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".