Evolution of COVID-19 Vaccination-related Topics on Twitter Analysis based on Hypothesis Building through Reading of Tweets

• Published in: Transactions of the Japanese Society for Artificial Intelligence Vol. 39; no. 5; pp. C-N93_1 - 10
• Main Authors: Kobayashi, Ryota, Yoshinaga, Naoki, Suda, Towa, Kitsuregawa, Masaru, Uno, Takeaki, Hashimoto, Takako, Nakayama, Yuri, Toyoda, Masashi, Takedomi, Yuka
• Format: Journal Article
• Language: English; Japanese
• Published: Tokyo The Japanese Society for Artificial Intelligence 01.09.2024; Japan Science and Technology Agency
• Subjects: Anxiety; COVID-19; COVID-19 vaccines; Fear; Hypotheses; Immunization; narrative; Narratives; Real time; social media analysis; Social networks; Twitter; vaccine; Vaccines; Japan
• ISSN: 1346-0714; 1346-8030
• DOI: 10.1527/tjsai.39-5_C-N93

The development of the COVID-19 vaccine and vaccination campaign was a significant concern for the people. In Japan, mass vaccination was initiated later than in other countries such as the United States, China, and Europe; however, vaccination coverage increased rapidly in this country, and in October 2021, Japan ranked 14th out of 229 countries in terms of COVID-19 vaccination rates. How did public opinion and concerns evolve in the face of the uncertain COVID-19 vaccination period? To address this question, we collected over 100 million Japanese vaccine-related tweets from January 1 to October 31, 2021. Using the Latent Dirichlet Allocation (LDA) model, we identified 15 main topics from a subset of tweets. We manually grouped these topics into four themes based on typical tweet content: (1) personal issues, (2) breaking news, (3) politics, and (4) conspiracy and humor. Then, we constructed hypotheses about topic evolution by interpreting the narrative underlying the tweets. We carefully read approximately 15,000 representative tweets and the percentage of a word in each topic to interpret the narrative. Finally, we verified the hypotheses by visualizing the change in the percentage of a word during the vaccination period. There are three main findings in this paper. First, the percentage of tweets containing “fear” and “anxiety” was highest in January 2021 and then decreased. This finding suggests that Twitter users felt fear and anxiety in January, when the vaccination schedule was unclear and that their negative feelings subsided once vaccination began. Second, the Twitter discourse reflected changes in the target population for vaccination, transitioning from discussions about health care workers in February to older individuals in April, and later to general Twitter users after July. Third, as the vaccination process progressed, users increasingly shared their real-time experiences through the tweets.