Unraveling Public Health Crises Across Stages: Understanding Twitter Emotions and Message Types During the California Measles Outbreak
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it