Characterizing the Propagation of Situational Information in Social Media During COVID-19 Epidemic: A Case Study on Weibo

Abstract

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.

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

“… Appel et al, 2020 , Diedrichs, 2000 , Eysenbach and o. M. I. R. , 2003 , Falagas et al, 2008 , Goldschmidt and o. P. N. , 2020 , Jain et al, 2018 , Jansen et al, 2010 , Li et al, 2020 , Meho et al, 2007 , Pastor and a. S. , 2020 , Rodrigues and das Dores, R. M., Camilo-Junior, C. G., & Rosa, T. C. J. I. j. o. m. i. , 2016 , Soliman et al, 2020 , Xu et al, 2020 .…”

Causal Modeling of Twitter Activity during COVID-19

“…Themes of previous studies that focus on exploration of, description of, correlation of, or predictive modeling with Twitter data during COVID-19 pandemic include sentiment analysis [17,[25][26][27][28], public attitude/interest measurement [21,[29][30][31], content analysis [15,[32][33][34][35][36], topic modeling [16,26,27,[37][38][39][40], analysis of misinformation, disinformation, or conspiracies [20,[41][42][43][44][45][46], outbreak detection or disease nowcasting/forecasting [18,19], and more [47][48][49][50][51][52]. Similarly, data from other social media channels (e.g., Weibo, Reddit, Facebook) or search engine statistics are utilized for parallel analyses related to COVID-19 pandemic as well [53][54][55][56][57][58][59][60][61]…”

What triggers online help-seeking retransmission during the COVID-19 period? Empirical evidence from Chinese social media

“…Due to the destructive power and highly infectious feature of this new coronavirus, many countries have taken various kinds of measures to prevent virus transmission. Meanwhile, people use social media to acquire and exchange multiple types of information at a historic and unprecedented scale [2]. So far, some researches have concentrated on the effects of social media in this particular period.…”