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.

[1]  Sarah Vieweg,et al.  Situational Awareness in Mass Emergency: A Behavioral and Linguistic Analysis of Microblogged Communications , 2012 .

[2]  James A. Hendler,et al.  Brokers or Bridges? Exploring Structural Holes in a Crowdsourcing System , 2016, Computer.

[3]  Matthias Hofer,et al.  Perceived bridging and bonding social capital on Twitter: Differentiating between followers and followees , 2013, Comput. Hum. Behav..

[4]  Edson C. Tandoc,et al.  Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines , 2015, Comput. Hum. Behav..

[5]  Roberto Di Pietro,et al.  Fame for sale: Efficient detection of fake Twitter followers , 2015, Decis. Support Syst..

[6]  María Martínez-Rojas,et al.  Twitter as a tool for the management and analysis of emergency situations: A systematic literature review , 2018, Int. J. Inf. Manag..

[7]  Vincent A. Knight,et al.  Tweeting the terror: modelling the social media reaction to the Woolwich terrorist attack , 2014, Social Network Analysis and Mining.

[8]  Cindy K. Chung,et al.  Linguistic Inquiry and Word Count (LIWC): Pronounced “Luke,” . . . and Other Useful Facts , 2012 .

[9]  Yisheng Lv,et al.  A hybrid learning method for the data-driven design of linguistic dynamic systems , 2019, IEEE/CAA Journal of Automatica Sinica.

[10]  Katherine L. Milkman,et al.  Emotion and Virality: What Makes Online Content Go Viral? , 2013 .

[11]  Niloy Ganguly,et al.  Extracting Situational Information from Microblogs during Disaster Events: a Classification-Summarization Approach , 2015, CIKM.

[12]  Alfonso J. Pedraza-Martinez,et al.  Social Media for Disaster Management: Operational Value of the Social Conversation , 2019, Production and Operations Management.

[13]  Juan M. Corchado,et al.  A polarity analysis framework for Twitter messages , 2015, Appl. Math. Comput..

[14]  Fei-Yue Wang,et al.  Accurate and robust eye center localization via fully convolutional networks , 2019, IEEE/CAA Journal of Automatica Sinica.

[15]  James A. Hendler,et al.  A Study of the Human Flesh Search Engine: Crowd-Powered Expansion of Online Knowledge , 2010, Computer.

[16]  G. Leung,et al.  Nowcasting and forecasting the potential domestic and international spread of the 2019-nCoV outbreak originating in Wuhan, China: a modelling study , 2020, The Lancet.

[17]  Kripabandhu Ghosh,et al.  Utilizing microblogs for assisting post-disaster relief operations via matching resource needs and availabilities , 2019, Inf. Process. Manag..

[18]  James A. Hendler,et al.  The Chinese “Human Flesh” Web: the first decade and beyond , 2014 .

[19]  Ed H. Chi,et al.  Want to be Retweeted? Large Scale Analytics on Factors Impacting Retweet in Twitter Network , 2010, 2010 IEEE Second International Conference on Social Computing.

[20]  Sushil Jajodia,et al.  Detecting Automation of Twitter Accounts: Are You a Human, Bot, or Cyborg? , 2012, IEEE Transactions on Dependable and Secure Computing.

[21]  N. Bhuvana,et al.  Facebook and Whatsapp as disaster management tools during the Chennai (India) floods of 2015 , 2019, International Journal of Disaster Risk Reduction.

[22]  Céline Morin,et al.  Tracking online heroisation and blame in epidemics , 2020, The Lancet Public Health.

[23]  Lily Bui,et al.  Social Media, Rumors, and Hurricane Warning Systems in Puerto Rico , 2019, HICSS.

[24]  Fernando Diaz,et al.  Extracting information nuggets from disaster- Related messages in social media , 2013, ISCRAM.

[25]  Chen Sun,et al.  Proximity based automatic data annotation for autonomous driving , 2020, IEEE/CAA Journal of Automatica Sinica.

[26]  Michael J. Stern,et al.  Digital Inequality and Place: The Effects of Technological Diffusion on Internet Proficiency and Usage across Rural, Suburban, and Urban Counties , 2009 .

[27]  J. Pennebaker,et al.  The Psychological Meaning of Words: LIWC and Computerized Text Analysis Methods , 2010 .

[28]  Leysia Palen,et al.  Microblogging during two natural hazards events: what twitter may contribute to situational awareness , 2010, CHI.

[29]  Roman Beck,et al.  The Role of Social Media for Collective Behavior Development in response to Natural Disasters , 2018, ECIS.

[30]  Hichang Cho,et al.  A Social Network Contagion Theory of Risk Perception , 2003, Risk analysis : an official publication of the Society for Risk Analysis.

[31]  Ari Rappoport,et al.  What's in a hashtag?: content based prediction of the spread of ideas in microblogging communities , 2012, WSDM '12.

[32]  R. Seyfarth,et al.  Affiliation, empathy, and the origins of theory of mind , 2013, Proceedings of the National Academy of Sciences.

[33]  Danah Boyd,et al.  Tweeting from the Town Square: Measuring Geographic Local Networks , 2010, ICWSM.

[34]  Jun Tian,et al.  Characterizing information propagation patterns in emergencies: A case study with Yiliang Earthquake , 2018, Int. J. Inf. Manag..