Evolution of COVID-19 Vaccination-related Topics on Twitter

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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Publication Details

Journal
Transactions of the Japanese Society for Artificial Intelligence
Published
2024-08-31
DOI
https://doi.org/10.1527/tjsai.39-5_c-n93
Primary Topic
Misinformation and Its Impacts
Type
article
Field-Weighted Citation Impact
0.00
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article

Evolution of COVID-19 Vaccination-related Topics on Twitter

Masashi Toyoda, Yuka Takedomi, Ryota Kobayashi, Masaru Kitsuregawa et al.
Transactions of the Japanese Society for Artificial Intelligence
Misinformation and Its Impacts
article

Evolution of COVID-19 Vaccination-related Topics on Twitter

Masashi Toyoda, Yuka Takedomi, Ryota Kobayashi, Masaru Kitsuregawa, Towa Suda, Takako Hashimoto, Takeaki Uno, Naoki Yoshinaga, Yuri Nakayama
article en

Abstract

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.

Transactions of the Japanese Society for Artificial IntelligenceVol. 39(5)
National Institute of Informatics (JP), Research Organization of Information and Systems (JP), Chiba University of Commerce (JP), The University of Tokyo (JP)
Zero hunger
Openalex Percentile: Top 77%
Misinformation and Its Impacts
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