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COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining

To assess the general Japanese population's thoughts on coronavirus disease of 2019 related discrimination by Tweets. Tweets were retrieved from search queries using the keywords “health care providers and discrimination (no hashtags)” and “corona and rural area (no hashtags)” via the Twitter a...

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Autores principales: Suzuki, Reina, Iizuka, Yusuke, Lefor, Alan Kawarai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Lippincott Williams & Wilkins 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428692/
https://www.ncbi.nlm.nih.gov/pubmed/34516501
http://dx.doi.org/10.1097/MD.0000000000027105
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author Suzuki, Reina
Iizuka, Yusuke
Lefor, Alan Kawarai
author_facet Suzuki, Reina
Iizuka, Yusuke
Lefor, Alan Kawarai
author_sort Suzuki, Reina
collection PubMed
description To assess the general Japanese population's thoughts on coronavirus disease of 2019 related discrimination by Tweets. Tweets were retrieved from search queries using the keywords “health care providers and discrimination (no hashtags)” and “corona and rural area (no hashtags)” via the Twitter application programming interface. Subsequently, a text-mining analysis was conducted on tokenized text data. R version 4.0.2 was used for the analysis. In total, 51,906 tweets for “corona and health care providers”, 59,560 tweets for “corona and rural” were obtained between the search period of July 29, 2020 and September 30, 2020. The most common 20 words from the tokenized text data were translated to English. Word clouds with the original Japanese words are presented. Tweets for corona and health care providers did not suggest significant evidence of discrimination toward health care providers on Twitter. Results for corona and rural area, however, showed the unexpected word “murahachibu” (an outmoded word meaning ostracism), suggesting persistent strong social pressure to prevent bringing the disease to the community. This kind of pressure may not be supported by scientific facts. These results demonstrate the need for continued educational efforts to disseminate factual information to the public.
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spelling pubmed-84286922021-09-13 COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining Suzuki, Reina Iizuka, Yusuke Lefor, Alan Kawarai Medicine (Baltimore) 4900 To assess the general Japanese population's thoughts on coronavirus disease of 2019 related discrimination by Tweets. Tweets were retrieved from search queries using the keywords “health care providers and discrimination (no hashtags)” and “corona and rural area (no hashtags)” via the Twitter application programming interface. Subsequently, a text-mining analysis was conducted on tokenized text data. R version 4.0.2 was used for the analysis. In total, 51,906 tweets for “corona and health care providers”, 59,560 tweets for “corona and rural” were obtained between the search period of July 29, 2020 and September 30, 2020. The most common 20 words from the tokenized text data were translated to English. Word clouds with the original Japanese words are presented. Tweets for corona and health care providers did not suggest significant evidence of discrimination toward health care providers on Twitter. Results for corona and rural area, however, showed the unexpected word “murahachibu” (an outmoded word meaning ostracism), suggesting persistent strong social pressure to prevent bringing the disease to the community. This kind of pressure may not be supported by scientific facts. These results demonstrate the need for continued educational efforts to disseminate factual information to the public. Lippincott Williams & Wilkins 2021-09-10 /pmc/articles/PMC8428692/ /pubmed/34516501 http://dx.doi.org/10.1097/MD.0000000000027105 Text en Copyright © 2021 the Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial License 4.0 (CCBY-NC), where it is permissible to download, share, remix, transform, and buildup the work provided it is properly cited. The work cannot be used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc/4.0 (https://creativecommons.org/licenses/by-nc/4.0/) This article is made available via the PMC Open Access Subset for unrestricted re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the COVID-19 pandemic or until permissions are revoked in writing. Upon expiration of these permissions, PMC is granted a perpetual license to make this article available via PMC and Europe PMC, consistent with existing copyright protections.
spellingShingle 4900
Suzuki, Reina
Iizuka, Yusuke
Lefor, Alan Kawarai
COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining
title COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining
title_full COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining
title_fullStr COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining
title_full_unstemmed COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining
title_short COVID-19 related discrimination in Japan: A preliminary analysis utilizing text-mining
title_sort covid-19 related discrimination in japan: a preliminary analysis utilizing text-mining
topic 4900
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8428692/
https://www.ncbi.nlm.nih.gov/pubmed/34516501
http://dx.doi.org/10.1097/MD.0000000000027105
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