Evolution of COVID-19 Vaccination-related Topics on Twitter Analysis based on Hypothesis Building through Reading of Tweets
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 Octo...
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| Published in | Transactions of the Japanese Society for Artificial Intelligence Vol. 39; no. 5; pp. C-N93_1 - 10 |
|---|---|
| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | English Japanese |
| Published |
Tokyo
The Japanese Society for Artificial Intelligence
01.09.2024
Japan Science and Technology Agency |
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| Online Access | Get full text |
| ISSN | 1346-0714 1346-8030 |
| DOI | 10.1527/tjsai.39-5_C-N93 |
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 39-5_C-N93 |
| Author | Suda, Towa Yoshinaga, Naoki Kobayashi, Ryota Kitsuregawa, Masaru Takedomi, Yuka Nakayama, Yuri Uno, Takeaki Toyoda, Masashi Hashimoto, Takako |
| Author_xml | – sequence: 1 fullname: Kobayashi, Ryota organization: The University of Tokyo – sequence: 1 fullname: Yoshinaga, Naoki organization: The University of Tokyo – sequence: 1 fullname: Suda, Towa organization: National Institute of Informatics – sequence: 1 fullname: Kitsuregawa, Masaru organization: The University of Tokyo – sequence: 1 fullname: Uno, Takeaki organization: National Institute of Informatics – sequence: 1 fullname: Hashimoto, Takako organization: Chiba University of Commerce – sequence: 1 fullname: Nakayama, Yuri organization: The University of Tokyo – sequence: 1 fullname: Toyoda, Masashi organization: The University of Tokyo – sequence: 1 fullname: Takedomi, Yuka organization: National Institute of Informatics |
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| Cites_doi | 10.1145/1772690.1772777 10.1145/2684822.2685324 10.1093/ofid/ofaa258 10.1609/icwsm.v10i1.14717 10.2196/24435 10.1609/icwsm.v5i1.14149 10.2196/32335 10.1017/CBO9780511586576 10.1109/BigData52589.2021.9671982 10.1126/science.1167742 10.1609/icwsm.v15i1.18063 10.2196/41928 10.1038/s41598-022-07067-w 10.1145/2502081.2502117 10.1016/S2589-7500(20)30315-0 |
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| References | [Kudo 06] Kudo, T.: MeCab: Yet another part-of-speech and morphological analyzer, http://mecab.sourceforge.jp (2006) [Medford 20] Medford, R. J., Saleh, S. N., Sumarsono, A., Perl, T. M., and Lehmann, C. U.: An ”infodemic”: leveraging high-volume Twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak, Open Forum Infectious Diseases, Vol. 7, No. 7, ofaa258 (2020) [鳥海20] 鳥海不二夫, 榊剛史, 吉田光男：ソーシャルメディアを用いた新型コロナ禍における感情変化の分析, 人工知能学会論文誌, Vol. 35, No. 4, pp. F–K45 1–7 (2020) [Kobayashi 22] Kobayashi, R., Takedomi, Y., Nakayama, Y., Suda, T., Uno, T., Hashimoto, T., Toyoda, M., Yoshinaga, N., Kitsuregawa, M., and Rocha, L. E. C.: Evolution of public opinion on COVID-19 vaccination in Japan: Large-scale Twitter data analysis, Journal of Medical Internet Research, Vol. 24, No. 12, e41928 (2022) [Lyu 21] Lyu, J. C., Han, E. L., and Luli, G. K.: COVID-19 vaccine–related discussion on Twitter: Topic modeling and sentiment analysis, Journal of Medical Internet Research, Vol. 23, No. 6, e24435 (2021) [Althoff 13] Althoff, T., Borth, D., Hees, J., and Dengel, A.: Analysis and forecasting of trending topics in online media streams, in Proceedings of the 21st ACM International Conference on Multimedia, pp. 907–916 (2013) [Sakaki 10] Sakaki, T., Okazaki, M., and Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors, in Proceedings of the 19th International Conference on World Wide Web, pp. 851–860 (2010) [Hashimoto 21] Hashimoto, T., Uno, T., Takedomi, Y., Shepard, D., Toyoda, M., Yoshinaga, N., Kitsuregawa, M., and Kobayashi, R.: Two-stage clustering method for discovering people’s perceptions: A case study of the COVID-19 vaccine from Twitter, in 2021 IEEE International Conference on Big Data (Big Data), pp. 614–621 (2021) [Petrovic 11] Petrovic, S., Osborne, M., and Lavrenko, V.: Rt to win! predicting message propagation in Twitter, in Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5, pp. 586–589 (2011) [成山09] 成山重子：日本語の省略がわかる本誰が？誰に？何を？,明治書院(2009) [Crosby 03] Crosby, A. W.: America’s Forgotten Pandemic: The Influenza of 1918, Cambridge University Press, 2 edition (2003) [Kobayashi 16] Kobayashi, R. and Lambiotte, R.: Tideh: Timedependent Hawkes process for predicting retweet dynamics, in Proceedings of the International AAAI Conference on Web and Social Media, Vol. 10, pp. 191–200 (2016) [Niu 22] Niu, Q., Liu, J., Kato, M., Shinohara, Y., Matsumura, N., Aoyama, T., and Nagai-Tanima, M.: Public opinion and sentiment before and at the beginning of COVID-19 vaccinations in Japan: Twitter analysis, JMIR Infodemiology, Vol. 2, No. 1, e32335 (2022) [NHK 21] NHK, : コロナワクチン国内初の副反応の疑い富山県でじんましん発生, https://www3.nhk.or.jp/news/html/20210220/k10012878071000.html (2021), [accessed 2023-08-14] [OurWorld 21] Our World in Data., https://ourworldindata.org (2021), [accessed 2024-01-15] [大曽01] 大曽美恵子：感情を表す動詞・形容詞に関する一考察, 言語文化論集, Vol. 22, No. 2, pp. 21–30 (2001) [Tsao 21] Tsao, S.-F., Chen, H., Tisseverasinghe, T., Yang, Y., Li, L., and Butt, Z. A.: What social media told us in the time of COVID-19: a scoping review, The Lancet Digital Health, Vol. 3, No. 3, e175–e194 (2021) [Blei 03] Blei, D. M., Ng, A. Y., and Jordan, M. I.: Latent Dirichlet allocation, Journal of Machine Learning Research, Vol. 3, pp. 993–1022 (2003) [Kobayashi 21] Kobayashi, R., Gildersleve, P., Uno, T., and Lambiotte, R.: Modeling collective anticipation and response on Wikipedia, in Proceedings of the International AAAI Conference on Web and Social Media, Vol. 15, pp. 315–326 (2021) [R¨oder 15] R¨oder, M., Both, A., and Hinneburg, A.: Exploring the space of topic coherence measures, in Proceedings of the 8th ACM International Conference on Web Search and Data Mining, pp. 399–408 (2015) [Lazer 09] Lazer, D., Pentland, A., Adamic, L., Aral, S., Barab´asi, A.- L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al.: Computational social science, Science, Vol. 323,No. 5915, pp. 721–723 (2009) [Wu 22] Wu, Q., Sano, Y., Takayasu, H., and Takayasu, M.: Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19, Scientific Reports, Vol. 12, No. 1, 3120 (2022) 11 22 12 13 14 15 16 17 18 19 1 2 3 4 5 6 7 8 9 20 10 21 |
| References_xml | – reference: [Kobayashi 21] Kobayashi, R., Gildersleve, P., Uno, T., and Lambiotte, R.: Modeling collective anticipation and response on Wikipedia, in Proceedings of the International AAAI Conference on Web and Social Media, Vol. 15, pp. 315–326 (2021) – reference: [Lazer 09] Lazer, D., Pentland, A., Adamic, L., Aral, S., Barab´asi, A.- L., Brewer, D., Christakis, N., Contractor, N., Fowler, J., Gutmann, M., et al.: Computational social science, Science, Vol. 323,No. 5915, pp. 721–723 (2009) – reference: [Petrovic 11] Petrovic, S., Osborne, M., and Lavrenko, V.: Rt to win! predicting message propagation in Twitter, in Proceedings of the International AAAI Conference on Web and Social Media, Vol. 5, pp. 586–589 (2011) – reference: [Wu 22] Wu, Q., Sano, Y., Takayasu, H., and Takayasu, M.: Classification of endogenous and exogenous bursts in collective emotions based on Weibo comments during COVID-19, Scientific Reports, Vol. 12, No. 1, 3120 (2022) – reference: [Althoff 13] Althoff, T., Borth, D., Hees, J., and Dengel, A.: Analysis and forecasting of trending topics in online media streams, in Proceedings of the 21st ACM International Conference on Multimedia, pp. 907–916 (2013) – reference: [Hashimoto 21] Hashimoto, T., Uno, T., Takedomi, Y., Shepard, D., Toyoda, M., Yoshinaga, N., Kitsuregawa, M., and Kobayashi, R.: Two-stage clustering method for discovering people’s perceptions: A case study of the COVID-19 vaccine from Twitter, in 2021 IEEE International Conference on Big Data (Big Data), pp. 614–621 (2021) – reference: [Kudo 06] Kudo, T.: MeCab: Yet another part-of-speech and morphological analyzer, http://mecab.sourceforge.jp (2006) – reference: [成山09] 成山重子：日本語の省略がわかる本誰が？誰に？何を？,明治書院(2009) – reference: [Medford 20] Medford, R. J., Saleh, S. N., Sumarsono, A., Perl, T. M., and Lehmann, C. U.: An ”infodemic”: leveraging high-volume Twitter data to understand early public sentiment for the coronavirus disease 2019 outbreak, Open Forum Infectious Diseases, Vol. 7, No. 7, ofaa258 (2020) – reference: [大曽01] 大曽美恵子：感情を表す動詞・形容詞に関する一考察, 言語文化論集, Vol. 22, No. 2, pp. 21–30 (2001) – reference: [OurWorld 21] Our World in Data., https://ourworldindata.org (2021), [accessed 2024-01-15] – reference: [Tsao 21] Tsao, S.-F., Chen, H., Tisseverasinghe, T., Yang, Y., Li, L., and Butt, Z. A.: What social media told us in the time of COVID-19: a scoping review, The Lancet Digital Health, Vol. 3, No. 3, e175–e194 (2021) – reference: [Kobayashi 16] Kobayashi, R. and Lambiotte, R.: Tideh: Timedependent Hawkes process for predicting retweet dynamics, in Proceedings of the International AAAI Conference on Web and Social Media, Vol. 10, pp. 191–200 (2016) – reference: [鳥海20] 鳥海不二夫, 榊剛史, 吉田光男：ソーシャルメディアを用いた新型コロナ禍における感情変化の分析, 人工知能学会論文誌, Vol. 35, No. 4, pp. F–K45 1–7 (2020) – reference: [Kobayashi 22] Kobayashi, R., Takedomi, Y., Nakayama, Y., Suda, T., Uno, T., Hashimoto, T., Toyoda, M., Yoshinaga, N., Kitsuregawa, M., and Rocha, L. E. C.: Evolution of public opinion on COVID-19 vaccination in Japan: Large-scale Twitter data analysis, Journal of Medical Internet Research, Vol. 24, No. 12, e41928 (2022) – reference: [R¨oder 15] R¨oder, M., Both, A., and Hinneburg, A.: Exploring the space of topic coherence measures, in Proceedings of the 8th ACM International Conference on Web Search and Data Mining, pp. 399–408 (2015) – reference: [Crosby 03] Crosby, A. W.: America’s Forgotten Pandemic: The Influenza of 1918, Cambridge University Press, 2 edition (2003) – reference: [Blei 03] Blei, D. M., Ng, A. Y., and Jordan, M. I.: Latent Dirichlet allocation, Journal of Machine Learning Research, Vol. 3, pp. 993–1022 (2003) – reference: [Niu 22] Niu, Q., Liu, J., Kato, M., Shinohara, Y., Matsumura, N., Aoyama, T., and Nagai-Tanima, M.: Public opinion and sentiment before and at the beginning of COVID-19 vaccinations in Japan: Twitter analysis, JMIR Infodemiology, Vol. 2, No. 1, e32335 (2022) – reference: [Sakaki 10] Sakaki, T., Okazaki, M., and Matsuo, Y.: Earthquake shakes Twitter users: real-time event detection by social sensors, in Proceedings of the 19th International Conference on World Wide Web, pp. 851–860 (2010) – reference: [Lyu 21] Lyu, J. C., Han, E. L., and Luli, G. K.: COVID-19 vaccine–related discussion on Twitter: Topic modeling and sentiment analysis, Journal of Medical Internet Research, Vol. 23, No. 6, e24435 (2021) – reference: [NHK 21] NHK, : コロナワクチン国内初の副反応の疑い富山県でじんましん発生, https://www3.nhk.or.jp/news/html/20210220/k10012878071000.html (2021), [accessed 2023-08-14] – ident: 2 – ident: 19 doi: 10.1145/1772690.1772777 – ident: 18 doi: 10.1145/2684822.2685324 – ident: 11 doi: 10.1093/ofid/ofaa258 – ident: 5 doi: 10.1609/icwsm.v10i1.14717 – ident: 10 doi: 10.2196/24435 – ident: 12 – ident: 17 doi: 10.1609/icwsm.v5i1.14149 – ident: 14 doi: 10.2196/32335 – ident: 3 doi: 10.1017/CBO9780511586576 – ident: 13 – ident: 16 – ident: 15 – ident: 4 doi: 10.1109/BigData52589.2021.9671982 – ident: 9 doi: 10.1126/science.1167742 – ident: 6 doi: 10.1609/icwsm.v15i1.18063 – ident: 7 doi: 10.2196/41928 – ident: 22 doi: 10.1038/s41598-022-07067-w – ident: 1 doi: 10.1145/2502081.2502117 – ident: 8 – ident: 20 – ident: 21 doi: 10.1016/S2589-7500(20)30315-0 |
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| SubjectTerms | Anxiety COVID-19 COVID-19 vaccines Fear Hypotheses Immunization narrative Narratives Real time social media analysis Social networks vaccine Vaccines |
| Subtitle | Analysis based on Hypothesis Building through Reading of Tweets |
| Title | Evolution of COVID-19 Vaccination-related Topics on Twitter |
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