Understanding COVID-19 vaccine hesitancy of different regions in the post-epidemic era: A causality deep learning approach.

Objective: This paper aims to understand vaccine hesitancy in the post-epidemic era by analyzing texts related to vaccine reviews and public attitudes toward three prominent vaccine brands: Sinovac, AstraZeneca, and Pfizer, and exploring the relationship of vaccine hesitancy with the prevalence of epidemics in different regions. Methods: We collected 165629 Twitter user comments associated with the vaccine brands. The comments were labeled based on willingness and attitude toward vaccination. We utilize a causality deep learning model, the Bert multi-channel convolutional neural network (BertMCNN), to predict users' willingness and attitude mutually. Results: When applied to the provided dataset, the proposed BertMCNN model demonstrated superior performance to traditional machine learning algorithms and other deep learning models. It is worth noting that after March 2022, the public was more hesitant about the Sinovac vaccines. Conclusions: This study reveals a connection between vaccine hesitancy and the prevalence of the epidemic in different regions. The analytical results obtained from this method can assist governmental health departments in making informed decisions regarding vaccination strategies.