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COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication
BACKGROUND: COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test t...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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JMIR Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108572/ https://www.ncbi.nlm.nih.gov/pubmed/33684054 http://dx.doi.org/10.2196/23272 |
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author | Park, Sungkyu Han, Sungwon Kim, Jeongwook Molaie, Mir Majid Vu, Hoang Dieu Singh, Karandeep Han, Jiyoung Lee, Wonjae Cha, Meeyoung |
author_facet | Park, Sungkyu Han, Sungwon Kim, Jeongwook Molaie, Mir Majid Vu, Hoang Dieu Singh, Karandeep Han, Jiyoung Lee, Wonjae Cha, Meeyoung |
author_sort | Park, Sungkyu |
collection | PubMed |
description | BACKGROUND: COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. OBJECTIVE: This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. METHODS: We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time–topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. RESULTS: This research found that each government’s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. CONCLUSIONS: This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic. |
format | Online Article Text |
id | pubmed-8108572 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-81085722021-05-13 COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication Park, Sungkyu Han, Sungwon Kim, Jeongwook Molaie, Mir Majid Vu, Hoang Dieu Singh, Karandeep Han, Jiyoung Lee, Wonjae Cha, Meeyoung J Med Internet Res Original Paper BACKGROUND: COVID-19, caused by SARS-CoV-2, has led to a global pandemic. The World Health Organization has also declared an infodemic (ie, a plethora of information regarding COVID-19 containing both false and accurate information circulated on the internet). Hence, it has become critical to test the veracity of information shared online and analyze the evolution of discussed topics among citizens related to the pandemic. OBJECTIVE: This research analyzes the public discourse on COVID-19. It characterizes risk communication patterns in four Asian countries with outbreaks at varying degrees of severity: South Korea, Iran, Vietnam, and India. METHODS: We collected tweets on COVID-19 from four Asian countries in the early phase of the disease outbreak from January to March 2020. The data set was collected by relevant keywords in each language, as suggested by locals. We present a method to automatically extract a time–topic cohesive relationship in an unsupervised fashion based on natural language processing. The extracted topics were evaluated qualitatively based on their semantic meanings. RESULTS: This research found that each government’s official phases of the epidemic were not well aligned with the degree of public attention represented by the daily tweet counts. Inspired by the issue-attention cycle theory, the presented natural language processing model can identify meaningful transition phases in the discussed topics among citizens. The analysis revealed an inverse relationship between the tweet count and topic diversity. CONCLUSIONS: This paper compares similarities and differences of pandemic-related social media discourse in Asian countries. We observed multiple prominent peaks in the daily tweet counts across all countries, indicating multiple issue-attention cycles. Our analysis identified which topics the public concentrated on; some of these topics were related to misinformation and hate speech. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic. JMIR Publications 2021-03-16 /pmc/articles/PMC8108572/ /pubmed/33684054 http://dx.doi.org/10.2196/23272 Text en ©Sungkyu Park, Sungwon Han, Jeongwook Kim, Mir Majid Molaie, Hoang Dieu Vu, Karandeep Singh, Jiyoung Han, Wonjae Lee, Meeyoung Cha. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 16.03.2021. https://creativecommons.org/licenses/by/4.0/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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Park, Sungkyu Han, Sungwon Kim, Jeongwook Molaie, Mir Majid Vu, Hoang Dieu Singh, Karandeep Han, Jiyoung Lee, Wonjae Cha, Meeyoung COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication |
title | COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication |
title_full | COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication |
title_fullStr | COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication |
title_full_unstemmed | COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication |
title_short | COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication |
title_sort | covid-19 discourse on twitter in four asian countries: case study of risk communication |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8108572/ https://www.ncbi.nlm.nih.gov/pubmed/33684054 http://dx.doi.org/10.2196/23272 |
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