Emotional reactions towards vaccination during the emergence of the Omicron variant: Insights from twitter analysis in South Africa
Bibliographic record
Abstract
The emergence of the Omicron variant triggered intense emotional reactions toward vaccination in South Africa, particularly evident on platforms like Twitter. These emotions have the potential to significantly influence vaccine confidence and uptake, posing a challenge for public health efforts. However, existing research lacks a detailed understanding of how emotional dynamics during variant-specific outbreaks, such as Omicron, impact vaccination rates, especially at a province level. This gap limits the ability of policymakers to design targeted interventions. Our study addresses this problem by analyzing emotional reactions to vaccination during the Omicron outbreak using geotagged Twitter data and the Text2emotion pre-trained model. We validated the model by hand-labeling a random 10% of tweets and comparing results with BERT-labeled tweets, finding no significant differences ( p < 0 . 001 for hand-labeled, p = 0 . 002 for BERT). Using statistical methods such as χ 2 , Mann–Whitney U, Granger causality, and Jaccard similarity, we identified a strong association between emotional intensities in vaccine-related posts and vaccination rates during the Omicron period ( p < 0 . 04 ) in specific provinces. Additionally, Latent Dirichlet Allocation (LDA) was employed for topic modeling, revealing variations in emotional reactions across topics and provinces before and during the Omicron variant. Our findings provide actionable insights for health policy-making by highlighting the role of emotional dynamics in vaccine acceptance and offering a province-level analysis of Twitter discussions. This study demonstrates the potential of social media data to understand public sentiment during disease outbreaks and serves as a valuable reference for future academic research. • Strong link between Twitter emotions and vaccination rates in South Africa during Omicron. • Text2Emotion model validated for reliable emotion classification in vaccine-related tweets. • Province-level Twitter analysis offers insights for health policy and future research.
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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 | 0.003 |
| 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 | 0.000 |
| 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".