直近一年間の累計
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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
キーワード
Twitter®
social media
text mining
COVID-19
influenza
資料タイプ
学術雑誌論文
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.
掲載誌名
Vaccines
ISSN
2076393X
出版者
MDPI
11
8
開始ページ
1327
発行日
2023-08-05
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/).
EDB ID
出版社版DOI
出版社版URL
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
医学系