Cargando…

Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining

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 202...

Descripción completa

Detalles Bibliográficos
Autores principales: Mori, Yoshiro, Miyatake, Nobuyuki, Suzuki, Hiromi, Mori, Yuka, Okada, Setsuo, Tanimoto, Kiyotaka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458112/
https://www.ncbi.nlm.nih.gov/pubmed/37631895
http://dx.doi.org/10.3390/vaccines11081327
_version_ 1785097089055719424
author Mori, Yoshiro
Miyatake, Nobuyuki
Suzuki, Hiromi
Mori, Yuka
Okada, Setsuo
Tanimoto, Kiyotaka
author_facet Mori, Yoshiro
Miyatake, Nobuyuki
Suzuki, Hiromi
Mori, Yuka
Okada, Setsuo
Tanimoto, Kiyotaka
author_sort Mori, Yoshiro
collection PubMed
description 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.
format Online
Article
Text
id pubmed-10458112
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-104581122023-08-27 Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining Mori, Yoshiro Miyatake, Nobuyuki Suzuki, Hiromi Mori, Yuka Okada, Setsuo Tanimoto, Kiyotaka Vaccines (Basel) Article 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. MDPI 2023-08-05 /pmc/articles/PMC10458112/ /pubmed/37631895 http://dx.doi.org/10.3390/vaccines11081327 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. 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/).
spellingShingle Article
Mori, Yoshiro
Miyatake, Nobuyuki
Suzuki, Hiromi
Mori, Yuka
Okada, Setsuo
Tanimoto, Kiyotaka
Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining
title Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining
title_full Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining
title_fullStr Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining
title_full_unstemmed Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining
title_short Comparison of Impressions of COVID-19 Vaccination and Influenza Vaccination in Japan by Analyzing Social Media Using Text Mining
title_sort comparison of impressions of covid-19 vaccination and influenza vaccination in japan by analyzing social media using text mining
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10458112/
https://www.ncbi.nlm.nih.gov/pubmed/37631895
http://dx.doi.org/10.3390/vaccines11081327
work_keys_str_mv AT moriyoshiro comparisonofimpressionsofcovid19vaccinationandinfluenzavaccinationinjapanbyanalyzingsocialmediausingtextmining
AT miyatakenobuyuki comparisonofimpressionsofcovid19vaccinationandinfluenzavaccinationinjapanbyanalyzingsocialmediausingtextmining
AT suzukihiromi comparisonofimpressionsofcovid19vaccinationandinfluenzavaccinationinjapanbyanalyzingsocialmediausingtextmining
AT moriyuka comparisonofimpressionsofcovid19vaccinationandinfluenzavaccinationinjapanbyanalyzingsocialmediausingtextmining
AT okadasetsuo comparisonofimpressionsofcovid19vaccinationandinfluenzavaccinationinjapanbyanalyzingsocialmediausingtextmining
AT tanimotokiyotaka comparisonofimpressionsofcovid19vaccinationandinfluenzavaccinationinjapanbyanalyzingsocialmediausingtextmining