Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model

Jianping Zhu, Futian Weng (), Muni Zhuang (), Xin Lu, Xu Tan, Songjie Lin and Ruoyi Zhang
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Jianping Zhu: National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
Futian Weng: National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
Muni Zhuang: National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
Xin Lu: College of Systems Engineering, National University of Defense Technology, Changsha 410073, China
Xu Tan: Career-Oriented Multidisciplinary Education Center, Shenzhen Institiute of Information Technology, Shenzhen 518172, China
Songjie Lin: Career-Oriented Multidisciplinary Education Center, Shenzhen Institiute of Information Technology, Shenzhen 518172, China
Ruoyi Zhang: Columbia College of Art and Science, George Washington University, Washington, DC 20052, USA

IJERPH, 2022, vol. 19, issue 20, 1-26

Abstract: The COVID-19 pandemic has created unprecedented burdens on people’s health and subjective well-being. While countries around the world have established models to track and predict the affective states of COVID-19, identifying the topics of public discussion and sentiment evolution of the vaccine, particularly the differences in topics of concern between vaccine-support and vaccine-hesitant groups, remains scarce. Using social media data from the two years following the outbreak of COVID-19 (23 January 2020 to 23 January 2022), coupled with state-of-the-art natural language processing (NLP) techniques, we developed a public opinion analysis framework (BertFDA). First, using dynamic topic clustering on Weibo through the latent Dirichlet allocation (LDA) model, a total of 118 topics were generated in 24 months using 2,211,806 microblog posts. Second, by building an improved Bert pre-training model for sentiment classification, we provide evidence that public negative sentiment continued to decline in the early stages of COVID-19 vaccination. Third, by modeling and analyzing the microblog posts from the vaccine-support group and the vaccine-hesitant group, we discover that the vaccine-support group was more concerned about vaccine effectiveness and the reporting of news, reflecting greater group cohesion, whereas the vaccine-hesitant group was particularly concerned about the spread of coronavirus variants and vaccine side effects. Finally, we deployed different machine learning models to predict public opinion. Moreover, functional data analysis (FDA) is developed to build the functional sentiment curve, which can effectively capture the dynamic changes with the explicit function. This study can aid governments in developing effective interventions and education campaigns to boost vaccination rates.

Keywords: COVID-19 vaccine; sentiment analysis; topic model; Bert; functional data analysis (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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