Analyzing social media discourse of avian influenza outbreaks
Bibliographic record
Abstract
The ongoing avian influenza outbreaks have had significant implications for the global poultry industry in addition to a wide range of wild birds and mammals. To enhance our understanding of public perceptions and reactions during such outbreaks, the present study examined social media discourse surrounding avian influenza on X (formerly known as Twitter). By employing advanced large language models, including DistilBERT for post filtering (average 89.5% accuracy via 5-fold cross-validation) along with Mixtral-8x7B, BERTopic, and RoBERTa for sentiment and topic/user analysis, this research categorizes the discussions and sentiments expressed by users over time. Our analysis focused on three aspects: main topics, sentiment, and temporal patterns of user engagement surrounding avian influenza outbreaks. Sentiment analysis revealed that a majority of posts related to economic impact (81.2%), wildlife (71.7%), and human cases (67.9%) expressed negative sentiment. Through topic modeling, prevalent topics of concern were identified in discussions, including concerns about transmission to humans and mammals, as well as issues related to food security and food prices. Additionally, the analysis of user engagement patterns showed distinct categories of users and highlighted the contributions of top users in shaping the discourse. Emotion analysis showed that over 80% of posts on major topics conveyed emotions such as anger, sadness, and fear, especially during periods of high case reports. The present study underscores the potential of social media analysis to understand public reactions to avian influenza outbreaks and to facilitate effective responses to public concerns and needs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".