A Cross-National Study of Fear Appeal Messages in YouTube Trending Videos About COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic has underlined the need for investigating the prevalence and nature of health communication on social media. Applying the Extended Parallel Process Model, this study analyzes the use of fear appeals in 2,152 YouTube trending videos across six countries (the United States, Brazil, Russia, Taiwan, Canada, and New Zealand) from January to May 2020. The findings reveal that, during the early stage of the outbreak, COVID-19-themed videos gained early attention in Taiwan but encountered a prolonged delay in the United States and Brazil. Specifically, COVID-19 videos featured the least in Brazil’s trending list. The results from a supervised machine learning coding approach further suggest that videos’ threat levels exceeded efficacy beliefs across all countries. This imbalance of threat–efficacy messages was most significant in hard-hit countries Brazil and Russia, which social media may run the risk of feeding fear to the public agenda. These findings alert content creators and social media platforms to create a threat–efficacy equilibrium, prioritizing content that promotes a sense of self- and community efficacy and increases people’s belief that effective protective actions are available.
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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 | 0.001 | 0.007 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".