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人工知能学会論文誌
Online ISSN : 1346-8030
Print ISSN : 1346-0714
ISSN-L : 1346-0714
原著論文
Twitterにおける新型コロナワクチンに関する話題の変化
ツイート本文の読解を通じた仮説構築による分析
武富 有香中山 悠理須田 永遠宇野 毅明橋本 隆子豊田 正史吉永 直樹 東京大学">喜連川 優 東京大学数理・情報教育研究センター">小林 亮太
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ジャーナル フリー

2024 年 39 巻 5 号 p. C-N93_1-10

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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.

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© 人工知能学会2024
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