#Outbreak: An Exploration of Twitter metadata as a means to supplement influenza surveillance in Canada during the 2013-2014 influenza season
Bibliographic record
Abstract
This study explored the utility of Twitter metadata as it relates to influenza surveillance in Canada. Twitter metadata posted between July 2013 and August 2014 containing influenza-related keywords (e.g. influenza, flu, cough) was analyzed using a variety of methodologies. Predictive regression models demonstrated differential utility of specific keywords; Tweets containing several keywords were strongly associated with influenza activity (flu, influenza, grippe), whereas a weaker association was observed with Tweets containing other keywords (e.g. cough, fever). Correlation analysis demonstrated that non-retweets and Tweets that did not contain a URL link were better correlated with influenza cases than retweets and Tweets containing a URL link, respectively. Geospatial cluster analysis showed that Twitter metadata could be used to identify local clusters of influenza-related Twitter chatter; clusters matched traditional surveillance reports in both space and time. Geospatial cluster analysis also identified clusters in areas not reported by the national Fluwatch program.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".