Mixed Sentiment Upon Globally Praised Concept of One Health: Gauging Responses using Twitter
Bibliographic record
Abstract
The concept of One Health, which has been prioritized and integrated into national strategies in developed countries as part of their sustainable development goals (SDGs), is often overlooked in developing countries, leading to unpreparedness for outbreaks. To understand global responses to One Health, we evaluated Twitter data, a microblogging social media platform with over 50 million users worldwide. Our analysis revealed that the top most tweeted words related to One Health were "onthealth", "fordnation", and "celliottability", which showed an association with Canada-based institutions and individuals, indicating Canada's leading role in implementing One Health strategies. We also found that One Health was linked to positive, negative, and neutral sentiments on Twitter. Overall, our results demonstrate that One Health triggers sentiment-polarized responses, and Twitter provides a valuable tool for gauging public sentiment and considering it in shaping One Health norms in society.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".