Rumors on Social Media During Emergencies

During and after a disaster, victims and others often take to social media sites to share information about conditions, aid, resources and the like. But well-intentioned users can spread rumors that are later found to be false, as they did following the 2011 Great East Japan earthquake, which hampered rescue operations and confused people. To improve the quality of information on social media, we study methods for integrating information provided by crowds in social media environments. In this paper, we review some results from our research showing that crowdsourced critical-thinking and veracity evaluation can be effective in curbing the spread of false information on social media. These findings suggest that crowds can help triage information in order to support the discovery of relevant information on social media during and after emergencies.

[1]  C. Haythornthwaite,et al.  Enabling Community Through Social Media , 2013, Journal of medical Internet research.

[2]  Rongjuan Chen,et al.  Perspective Matters: Sharing of Crisis Information in Social Media , 2013, 2013 46th Hawaii International Conference on System Sciences.

[3]  Anupam Joshi,et al.  Faking Sandy: characterizing and identifying fake images on Twitter during Hurricane Sandy , 2013, WWW.

[4]  Barbara Poblete,et al.  Information credibility on twitter , 2011, WWW.

[5]  Prashant Bordia,et al.  Rumors Denials as Persuasive Messages: Effects of Personal Relevance, Source, and Message Characteristics , 2005 .

[6]  Panagiotis G. Ipeirotis,et al.  Running Experiments on Amazon Mechanical Turk , 2010, Judgment and Decision Making.

[7]  Huaye Li,et al.  The Influence of Collective Opinion on True-False Judgment and Information-Sharing Decision , 2013, CogSci.

[8]  Jacob Ratkiewicz,et al.  Truthy: mapping the spread of astroturf in microblog streams , 2010, WWW.

[9]  Ralph L. Rosnow,et al.  Factors influencing rumor spreading: Replication and extension. , 1988 .

[10]  Michael Koller,et al.  Rebutting accusations: When does it work, when does it fail? , 1993 .

[11]  Michael D. Buhrmester,et al.  Amazon's Mechanical Turk , 2011, Perspectives on psychological science : a journal of the Association for Psychological Science.

[12]  Rongjuan Chen,et al.  Feelings and Perspective Matter: Sharing of Crisis Information in Social Media , 2013, 2014 47th Hawaii International Conference on System Sciences.

[13]  Matthew J. Salganik,et al.  Experimental Study of Inequality and Unpredictability in an Artificial Cultural Market , 2006, Science.

[14]  Kate Starbird,et al.  Rumors, False Flags, and Digital Vigilantes: Misinformation on Twitter after the 2013 Boston Marathon Bombing , 2014 .

[15]  Huaye Li,et al.  Computing the Veracity of Information through Crowds: A Method for Reducing the Spread of False Messages on Social Media , 2015, 2015 48th Hawaii International Conference on System Sciences.

[16]  Yasuaki Sakamoto,et al.  Toward a Social-Technological System that Inactivates False Rumors through the Critical Thinking of Crowds , 2013, 2013 46th Hawaii International Conference on System Sciences.

[17]  Todd M. Gureckis,et al.  CUNY Academic , 2016 .

[18]  Prashant Bordia,et al.  Source Characteristics in Denying Rumors of Organizational Closure: Honesty Is the Best Policy , 2000 .

[19]  Yasuaki Sakamoto,et al.  The Impact of Collective Opinion on Online Judgment , 2010 .