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

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  • Jianping Zhu

    (National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China
    School of Management, Xiamen University, Xiamen 361005, China)

  • Futian Weng

    (National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China
    School of Medicine, Xiamen University, Xiamen 361005, China)

  • Muni Zhuang

    (National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
    Data Mining Research Center, Xiamen University, Xiamen 361005, China
    School of 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)

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.

Suggested Citation

  • Jianping Zhu & Futian Weng & Muni Zhuang & Xin Lu & Xu Tan & Songjie Lin & Ruoyi Zhang, 2022. "Revealing Public Opinion towards the COVID-19 Vaccine with Weibo Data in China: BertFDA-Based Model," IJERPH, MDPI, vol. 19(20), pages 1-26, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:20:p:13248-:d:942254
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    References listed on IDEAS

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    2. Jian Shi & Hanxiao Wang, 2023. "Examining the Intermedia Agenda Setting Effects amid the Changsheng Vaccine Crisis: A Computational Approach," IJERPH, MDPI, vol. 20(5), pages 1-12, February.

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