Twitter Followers of Canadian Political and Health Authorities during the COVID-19 Pandemic: What Are Their Activity and Interests?
Bibliographic record
Abstract
Abstract I examined the use of Twitter during the COVID-19 pandemic to find out how many Twitter users started to follow relevant Canadian political and health authorities, and I investigated their activity and interests. To this end, I analyzed 398,037 Twitter accounts. The results reveal that the Twitter accounts of relevant authorities gained a significant number of new Twitter followers during the pandemic. The Twitter users who joined during the pandemic were rather passive; they tweeted and liked fewer tweets than Twitter users who registered in the months prior to the pandemic. They also chose to follow Twitter accounts predominantly related to news, politics and governmental agencies. These findings suggest that during the pandemic, numerous information-seeking citizens joined Twitter for the purpose of obtaining information about public health matters, which in turn suggests that authorities should incorporate Twitter into their information dissemination tools, especially during emergencies, to meet the public demand for information.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".