WEKO3
アイテム
Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study
http://hdl.handle.net/10061/0002000094
http://hdl.handle.net/10061/000200009436fcae32-d2ba-4f8c-822a-6d50e169d520
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-01-11 | |||||||||
| タイトル | ||||||||||
| タイトル | Monitoring Mentions of COVID-19 Vaccine Side Effects on Japanese and Indonesian Twitter: Infodemiological Study | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | COVID-19 | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | vaccine | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | COVID-19 vaccine | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Pfizer | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Moderna | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | vaccine side effects | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | side effects | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | ||||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | logistic regression | |||||||||
| 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 |
Ferawati, Kiki
× Ferawati, Kiki
× Liew, Kongmeng
× 荒牧, 英治× 若宮, 翔子 |
|||||||||
| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | Background: The year 2021 was marked by vaccinations against COVID-19, which spurred wider discussion among the general population, with some in favor and some against vaccination. Twitter, a popular social media platform, was instrumental in providing information about the COVID-19 vaccine and has been effective in observing public reactions. We focused on tweets from Japan and Indonesia, 2 countries with a large Twitter-using population, where concerns about side effects were consistently stated as a strong reason for vaccine hesitancy. Objective: This study aimed to investigate how Twitter was used to report vaccine-related side effects and to compare the mentions of these side effects from 2 messenger RNA (mRNA) vaccine types developed by Pfizer and Moderna, in Japan and Indonesia. Methods: We obtained tweet data from Twitter using Japanese and Indonesian keywords related to COVID-19 vaccines and their side effects from January 1, 2021, to December 31, 2021. We then removed users with a high frequency of tweets and merged the tweets from multiple users as a single sentence to focus on user-level analysis, resulting in a total of 214,165 users (Japan) and 12,289 users (Indonesia). Then, we filtered the data to select tweets mentioning Pfizer or Moderna only and removed tweets mentioning both. We compared the side effect counts to the public reports released by Pfizer and Moderna. Afterward, logistic regression models were used to compare the side effects for the Pfizer and Moderna vaccines for each country. Results: We observed some differences in the ratio of side effects between the public reports and tweets. Specifically, fever was mentioned much more frequently in tweets than would be expected based on the public reports. We also observed differences in side effects reported between Pfizer and Moderna vaccines from Japan and Indonesia, with more side effects reported for the Pfizer vaccine in Japanese tweets and more side effects with the Moderna vaccine reported in Indonesian tweets. Conclusions: We note the possible consequences of vaccine side effect surveillance on Twitter and information dissemination, in that fever appears to be over-represented. This could be due to fever possibly having a higher severity or measurability, and further implications are discussed. |
|||||||||
| 書誌情報 |
en : JMIR Infodemiology 巻 2, 号 2, 発行日 2022-10-04 |
|||||||||
| artnum | ||||||||||
| 値 | e39504 | |||||||||
| 出版者 | ||||||||||
| 出版者 | JMIR Publications | |||||||||
| ISSN | ||||||||||
| 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/10.2196/39504 | |||||||||
| 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/2022/2/e39504 | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | https://creativecommons.org/licenses/by/4.0/ | |||||||||
| 権利情報 | cKiki Ferawati, Kongmeng Liew, Eiji Aramaki, Shoko Wakamiya. Originally published in JMIR Infodemiology (https://infodemiology.jmir.org), 04.10.2022. 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 | |||||||||
