Reactions to the Coronavirus: A Content Analysis Examining the Extent to Which Media Shapes Public Reactions in Response to COVID-19
Bibliographic record
Abstract
This qualitative study explored the extent to which mass media exposure shapes public reactions in response to the COVID-19 pandemic. A purposive sampling procedure was used to employ a content analysis on a sample of 100 of the most recent comments that included reactions towards COVID-19 from a CBC news article. An open-coding procedure was utilized to examine any themes or categories present in the comments, and the frequency of occurrence of any themes or categories were recorded. Results showed that eight categories of reactions were present: Fear, Warnings, Frivolous, Anger, Hope, Inevitable, Science, and Environment. Further sub-categories were identified within the overarching themes of fear, warnings, frivolous, and anger. This study demonstrated that fear is the most prevalent reaction towards COVID-19, keeping in line with existing research that media exposure and its use of fear-mongering tactics play a central role in shaping public reactions in response to pandemics.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".