BROWSE

Related Scientist

sungkyu,park's photo.

sungkyu,park
데이터사이언스그룹
more info

ITEM VIEW & DOWNLOAD

COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication

Cited 0 time in webofscience Cited 0 time in scopus
402 Viewed 0 Downloaded
Title
COVID-19 Discourse on Twitter in Four Asian Countries: Case Study of Risk Communication
Author(s)
Sungkyu Park; Han, Sungwon; Kim, Jeongwook; Molaie, Mir Majid; Vu, Hoang Dieu; Karandeep Singh; Han, Jiyoung; Lee, Wonjae; Meeyoung Cha
Publication Date
2021-03-16
Journal
Journal of Medical Internet Research, v.23, no.3
Publisher
Journal of medical Internet Research
Abstract
©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.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.
URI
https://pr.ibs.re.kr/handle/8788114/10007
DOI
10.2196/23272
ISSN
1438-8871
Appears in Collections:
Pioneer Research Center for Mathematical and Computational Sciences(수리 및 계산과학 연구단) > Data Science Group(데이터 사이언스 그룹) > 1. Journal Papers (저널논문)
Files in This Item:
There are no files associated with this item.

qrcode

  • facebook

    twitter

  • Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.
해당 아이템을 이메일로 공유하기 원하시면 인증을 거치시기 바랍니다.

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse