Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo
During the ongoing outbreak of coronavirus disease (COVID-19), people use social media to acquire and exchange various types of information at a historic and unprecedented scale. Only the situational information are valuable for the public and authorities to response to the epidemic. Therefore, it is important to identify such situational information and to understand how it is being propagated on social media, so that appropriate information publishing strategies can be informed for the COVID-19 epidemic. This article sought to fill this gap by harnessing Weibo data and natural language processing techniques to classify the COVID-19-related information into seven types of situational information. We found specific features in predicting the reposted amount of each type of information. The results provide data-driven insights into the information need and public attention.
PROJECT OUTPUT VIDEO:
- System : Pentium i3 Processor
- Hard Disk : 500 GB.
- Monitor : 15’’ LED
- Input Devices : Keyboard, Mouse
- Ram : 2 GB
- Operating system : Windows 10.
- Coding Language : JAVA
- Tool : Netbeans 8.2
- Database : MYSQL
Lifang Li, Qingpeng Zhang , Member, IEEE, Xiao Wang, Member, IEEE, Jun Zhang , Senior Member, IEEE, Tao Wang, Tian-Lu Gao, Wei Duan, Kelvin Kam-fai Tsoi, and Fei-Yue Wang , Fellow, IEEE, “Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo”, IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS, VOL. 7, NO. 2, APRIL 2020.