In December, 2019, there was an outbreak of pneumonia associated with the 2019 novel coronavirus (COVID-19) in Wuhan, Hubei province in China. 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.
In December, 2019, there was an outbreak of pneumonia associated with the 2019 novel coronavirus (COVID-19) in Wuhan, Hubei province in China. On the afternoon of January 30th, Geneva, World Health Organization (WHO) declared the novel coronavirus outbreak in China as a Public Health Emergency of International Concern (PHEIC). As a growing number of confirmed cases of infections is reported, the Chinese government have 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, 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 are analyzed. And we performed a kernel density estimation using ArcGIS to identify the hot spots of public opinion.
Provided by the IKCEST Disaster Risk Reduction Knowledge Service System
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