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Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences
http://hdl.handle.net/10061/0002000729
http://hdl.handle.net/10061/000200072915a10235-5b14-43d7-8b4a-5dd2b0b122c0
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||||
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| 公開日 | 2024-12-27 | |||||||||||
| タイトル | ||||||||||||
| タイトル | Exploring the Impact of the COVID-19 Pandemic on Twitter in Japan: Qualitative Analysis of Disrupted Plans and Consequences | |||||||||||
| 言語 | ||||||||||||
| 言語 | eng | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | COVID-19 | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | natural language processing | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | NLP | |||||||||||
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| 主題Scheme | Other | |||||||||||
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| 主題Scheme | Other | |||||||||||
| 主題 | disrupted plans | |||||||||||
| キーワード | ||||||||||||
| 主題Scheme | Other | |||||||||||
| 主題 | concerns | |||||||||||
| 資源タイプ | ||||||||||||
| 資源タイプ | journal article | |||||||||||
| アクセス権 | ||||||||||||
| アクセス権 | open access | |||||||||||
| 著者 |
Kamba, Masaru
× Kamba, Masaru
× She, Wan Jou
× Ferawati, Kiki
× 若宮, 翔子× 荒牧, 英治 |
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| 抄録 | ||||||||||||
| 内容記述タイプ | Abstract | |||||||||||
| 内容記述 | Background: Despite being a pandemic, the impact of the spread of COVID-19 extends beyond public health, influencing areas such as the economy, education, work style, and social relationships. Research studies that document public opinions and estimate the long-term potential impact after the pandemic can be of value to the field. Objective: This study aims to uncover and track concerns in Japan throughout the COVID-19 pandemic by analyzing Japanese individuals’ self-disclosure of disruptions to their life plans on social media. This approach offers alternative evidence for identifying concerns that may require further attention for individuals living in Japan. Methods: We extracted 300,778 tweets using the query phrase Corona-no-sei (“due to COVID-19,” “because of COVID-19,” or “considering COVID-19”), enabling us to identify the activities and life plans disrupted by the pandemic. The correlation between the number of tweets and COVID-19 cases was analyzed, along with an examination of frequently co-occurring words. Results: The top 20 nouns, verbs, and noun plus verb pairs co-occurring with Corona no-sei were extracted. The top 5 keywords were graduation ceremony, cancel, school, work, and event. The top 5 verbs were disappear, go, rest, can go, and end. Our findings indicate that education emerged as the top concern when the Japanese government announced the first state of emergency. We also observed a sudden surge in anxiety about material shortages such as toilet paper. As the pandemic persisted and more states of emergency were declared, we noticed a shift toward long-term concerns, including careers, social relationships, and education. Conclusions: Our study incorporated machine learning techniques for disease monitoring through the use of tweet data, allowing the identification of underlying concerns (eg, disrupted education and work conditions) throughout the 3 stages of Japanese government emergency announcements. The comparison with COVID-19 case numbers provides valuable insights into the short- and long-term societal impacts, emphasizing the importance of considering citizens’ perspectives in policy-making and supporting those affected by the pandemic, particularly in the context of Japanese government decision-making. |
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| 書誌情報 |
en : JMIR Infodemiology 巻 4, 発行日 2024-04-01 |
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| 出版者 | ||||||||||||
| 出版者 | JMIR Publications | |||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||
| 収録物識別子 | 2564-1891 | |||||||||||
| 出版者版DOI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | DOI | |||||||||||
| 関連識別子 | https://doi.org/10.2196/49699 | |||||||||||
| 出版者版URI | ||||||||||||
| 関連タイプ | isReplacedBy | |||||||||||
| 識別子タイプ | URI | |||||||||||
| 関連識別子 | https://infodemiology.jmir.org/2024/1/e49699 | |||||||||||
| 権利 | ||||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||||
| 権利情報 | $00A9Masaru Kamba, Wan Jou She, Kiki Ferawati, Shoko Wakamiya, Eiji Aramaki. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 01.04.2024. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Infodemiology, is properly cited. The complete bibliographic information, a link to the original publication on https://infodemiology.jmir.org/, as well as this copyright and license information must be included. | |||||||||||
| 著者版フラグ | ||||||||||||
| 出版タイプ | NA | |||||||||||
