Unraveling Public Health Crises Across Stages: Understanding Twitter Emotions and Message Types During the California Measles Outbreak
Bibliographic record
Abstract
Social media can be used to assess public opinions and emotions during different stages of a crisis. Guided by the Crisis and Emergency Risk Communication (CERC) model, this study examined a systematic sample of 2,881 tweets from a corpus of over one million tweets posted during the initial, maintenance, and resolution stages of the 2015 California measles outbreak. It found that the public showed the greatest interest (as measured by the number of tweets and retweets) in the initial stage of the crisis, but their interest drastically declined afterward. The expression of humor/sarcasm was significantly more frequent in the initial stage than in the maintenance or resolutions stage, while the expression of reassurance increased significantly from the initial, maintenance, and resolution stage. The emotion of alarm/concern was most frequently expressed during the initial stage. For message types, the public were more likely to tweet about their personal opinions and less likely to tweet about resources during the initial stage. These findings allow public health professionals to better design messages in response to the public’s concerns and emotions during public health crises.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".