[3L1-GS-11] AI and Society

[3L1-GS-11-02] Evolution of Public Opinion on COVID-19 Vaccination in Japan

〇Yuka Takedomi¹, Yuri Nakayama², Towa Suda¹, Takeaki Uno¹, Takako Hashimoto³, Masashi Toyoda⁴, Naoki Yoshinaga⁴, Masaru Kitsuregawa¹, Luis E C Rocha^5,7, Ryota Kobayashi^2,6 (1. National Institute of Informatics, 2. Graduate School of Frontier Sciences, The University of Tokyo, 3. Chiba University of Commerce, 4. Institute of Industrial Science, The University of Tokyo, 5. Department of Economics, Ghent University, 6. Mathematics and Informatics Center, The University of Tokyo, 7. Department of Physics and Astronomy, Ghent University)

Keywords:Twitter, COVID-19, vaccination, topic analysis, time series analysis

This study aimed to identify the main themes in COVID-19 vaccine-related discussions on Twitter in Japan and track how the popularity of the tweeted themes evolved during the vaccination campaign. First, we collected all Japanese tweets (more than 110 million), including the word "vaccine," from January 1 to October 31, 2021. Then, we used Latent Dirichlet Allocation to perform automated topic modeling during the vaccination campaign. We identified 15 topics grouped into the following 4 themes: (1) personal issue, (2) breaking news, (3) politics, and (4) conspiracy and humor. In addition, we performed an interrupted time series regression analysis to evaluate the impact of 4 critical social events on public opinion. Our analysis showed that after the start of general public vaccination in June, the proportion of tweets on personal topics such as vaccination schedules, reports, and side reactions increased, while social issues such as vaccine policy, effectiveness, and related news decreased.