Fear in Media Headlines Increases Public Risk Perceptions but Decreases Preventive Behaviors: A Multi-Country Study During the COVID-19 Pandemic
Bibliographic record
Abstract
The perception of reality could matter more than reality itself when it comes to disease outbreaks. News media are important sources of information during global disease outbreaks, such as the COVID-19 pandemic. Drawing on theories of fear appeals and the social ecological model, we conducted multilevel modeling analyses to examine how media-level and community-level factors influenced the public’s risk perceptions of COVID-19 and frequencies of preventive behaviors in the United States, United Kingdom, Canada, Australia, and India. We combined a large-scale multi-wave cross-country survey (N = 161,374) with a COVID-19 media coverage archive (N = 10,015,187) to test these relationships. We found that fear in media headlines was positively correlated with people’s perceptions of risk but negatively correlated with frequencies of preventive behaviors, controlling for individual-, community-, and cultural-level factors. Similar patterns were consistently identified within each individual country. We also show that community factors interacted with the media environment to influence public risk perceptions and behaviors. Our findings highlight a strong mass media influence during the pandemic, and we discuss the implications of our findings for health communication during crisis times.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".