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Public opinion analysis for COVID-19

Maps of Kernel Density recent days

Topic statistics of China Weibo

Public opinion analysis for COVID-19

In December 2019, there was an outbreak of pneumonia associated with the 2019 novel coronavirus (COVID-19) in Wuhan, China. On January 30th, 2020, Geneva, World Health Organization (WHO) declared this novel coronavirus as a Public Health Emergency of International Concern (PHEIC). As a growing number of confirmed cases of infections is reported, the Chinese government has taken prompt response measures to curb the spread of the novel coronavirus (COVID-19).

Public risk communication activities have been carried out to improve public awareness and adoption of self-protection measures. With the rapid development of Internet, more and more people like to express their opinions and views on social media( e.g. Sina-Microblog), which provides an innovative approach to observe public opinion under emergencies in disaster events. The Disaster Risk Reduction Knowledge Service System of IKCEST analyzes public opinion during the novel coronavirus outbreak through integrating the social media analytics and GIS methods.

Microblog (http://us.weibo.com), a Twitter-like microblogging system, is the most popular microblogging service in China. Through the permitted data API of Sina Microblog, original Microblog messages are collected with “coronavirus” and “pneumonia” as the keywords since 00:00 on January 9, 2020. The following information was extracted: user ID, timestamp (i.e., the time when the message was posted), text (i.e., the text message posted by a user), and location information. Then, we analyzed the Microblog texts related to the novel coronavirus outbreak in terms of space and time. The temporal changes within an hour and one-day intervals are investigated. The spatial distribution on provincial levels of epidemic-related Microblog is analyzed. And we performed a kernel density estimation using ArcGIS to identify the hot spots of public opinion.

Using Social Media to Mine and Analyse Public Opinion Related to COVID-19 in China
This work aimed to identify public opinion during the COVID-19 outbreak from social media and analyse its spatial-temporal characteristics from January 9 to February 10, 2020 in China. A topic extraction and classification model was built to identify the topics of COVID-19-related Weibo and uncover public sentiments in response to COVID-19. A series of topics and temporal and spatial distributions were identified and discussed.
Spatiotemporal evolution and regional differences of public opinion in the prevention and control of COVID-19 epidemic in China from Jan.9 to Mar. 10, 2020