Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo
By Lifang Li, Qingpeng Zhang, Xiao Wang, J. Zhang, Tao Wang, Tian-Lu Gao, Wei Duan, K. Tsoi, Fei-yue Wang. IEEE Transactions on Computational Social Systems, 2020
388 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Sociology and Political Science, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/82b08336a874e37408ec3c0dbb1014661a3eaaad
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In Artificial Intelligence
Citations in the corpus
- Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review (2020, background): “Social media platforms are gaining remarkable influence than ever before and are considered one of the fastest growing information systems for social applications (Appel, Grewal, Hadi, & Stephen, 2020; Xu, Zayed, Lin, Wang, & Li, 2020).”