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 (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 are analyzed.
And we performed a kernel density estimation using ArcGIS to identify the hot spots of