Emotional reactions towards vaccination during the emergence of the Omicron variant: Insights from twitter analysis in South Africa

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.