Abstract
BackgroundCOVID-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.ObjectiveThis 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.MethodsWe 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.ResultsThis 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.ConclusionsThis 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.
Highlights
BackgroundThe COVID-19 pandemic has affected global health and the economy
This study showed that infodemics’ harm varied from country to country depending on public confidence in authorities
Our analyses focused on four Asian countries
Summary
The use of social media and the internet to seek and share information about the virus has increased rapidly [1,2], which makes them excellent media to examine for patterns of risk communication during a pandemic. During this time, one could observe how the intentional and unintentional spread of misinformation (here defined as unconfirmed or false information) jeopardized public health on such platforms. The sheer amount of data and the mixture of accurate and false information leaves people confused over which safety guidelines and health tips to follow This phenomenon has been called an infodemic [6]. These findings and the ability to quickly identify key topics can empower global efforts to fight against an infodemic during a pandemic
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
