ID | 118603 |
著者 |
Mori, Yoshiro
Kagawa University|Sakaide City Hospital
Miyatake, Nobuyuki
Kagawa University
Suzuki, Hiromi
Kagawa University
Okada, Setsuo
Sakaide City Hospital
Tanimoto, Kiyotaka
Sakaide City Hospital
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キーワード | Twitter®
social media
text mining
COVID-19
influenza
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資料タイプ |
学術雑誌論文
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抄録 | The aim of this study was to compare impressions of COVID-19 vaccination and influenza vaccination in Japan by analyzing social media (Twitter®) using a text-mining method. We obtained 10,000 tweets using the keywords “corona vaccine” and “influenza vaccine” on 15 December 2022 and 19 February 2023. We then counted the number of times the words were used and listed frequency of these words by a text-mining method called KH Coder. We also investigated concepts in the data using groups of words that often appeared together or groups of documents that contained the same words using multi-dimensional scaling (MDS). “Death” in relation to corona vaccine and “severe disease” for influenza vaccine were frequently used on 15 December 2022. The number of times the word “death” was used decreased, “after effect” was newly recognized for corona vaccine, and “severe disease” was not used in relation to influenza vaccine. Through this comprehensive analysis of social media data, we observed distinct variations in public perceptions of corona vaccination and influenza vaccination in Japan. These findings provide valuable insights for public health authorities and policymakers to better understand public sentiment and tailor their communication strategies accordingly.
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掲載誌名 |
Vaccines
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ISSN | 2076393X
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出版者 | MDPI
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巻 | 11
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号 | 8
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開始ページ | 1327
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発行日 | 2023-08-05
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権利情報 | This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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言語 |
eng
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著者版フラグ |
出版社版
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部局 |
医学系
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