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Deb K. Roy. Vosoughi, Soroush Massachusetts Institute of Technology. Department of Architecture. Program in Media Arts and Sciences. Program in Media Arts and Sciences (Massachusetts Institute of Technology) 2015-09-17T17:42:09Z 2015-09-17T17:42:09Z 2015 2015 http://hdl.handle.net/1721.1/98553 920678171 Thesis: Ph. D., Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2015. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (pages 135-147). The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations such as real-world emergencies can have harmful effects on individuals and society. This thesis develops models for automated detection and verification of rumors (unverified information) that propagate through Twitter. Detection of rumors about an event is achieved through classifying and clustering assertions made about that event. Assertions are classified through a speech-act classifier for Twitter developed for this thesis. The classifier utilizes a combination of semantic and syntactic features to identify assertions with 91% accuracy. To predict the veracity of rumors, we identify salient features of rumors by examining three aspects of information spread: linguistic style used to express rumors, characteristics of people involved in propagating information, and network propagation dynamics. The predicted veracity of a time series of these features extracted from a rumor (a collection of tweets) is generated using Hidden Markov Models. The verification algorithm was tested on 209 rumors representing 938,806 tweets collected from real-world events including the 2013 Boston Marathon bombings, the 2014 Ferguson unrest and the 2014 Ebola epidemic, and many other rumors reported on popular websites that document public rumors. The algorithm is able to predict the veracity of rumors with an accuracy of 75%. The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter. by Soroush Vosoughi. Ph.D. 147 pages eng Massachusetts Institute of Technology M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 Architecture. Program in Media Arts and Sciences. Automatic detection and verification of rumors on Twitter Thesis application/pdf false Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="895e70c3-113c-4b91-8d5a-8b8451426728"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Automatic detection and verification of rumors on Twitter</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2015</PublicationDate> <Authors> <Author> <DisplayName>Vosoughi, Soroush</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://dspace.mit.edu/handle/1721.1/7582</License> <Keyword>Architecture. Program in Media Arts and Sciences.</Keyword> <Abstract>The spread of malicious or accidental misinformation in social media, especially in time-sensitive situations such as real-world emergencies can have harmful effects on individuals and society. This thesis develops models for automated detection and verification of rumors (unverified information) that propagate through Twitter. Detection of rumors about an event is achieved through classifying and clustering assertions made about that event. Assertions are classified through a speech-act classifier for Twitter developed for this thesis. The classifier utilizes a combination of semantic and syntactic features to identify assertions with 91% accuracy. To predict the veracity of rumors, we identify salient features of rumors by examining three aspects of information spread: linguistic style used to express rumors, characteristics of people involved in propagating information, and network propagation dynamics. The predicted veracity of a time series of these features extracted from a rumor (a collection of tweets) is generated using Hidden Markov Models. The verification algorithm was tested on 209 rumors representing 938,806 tweets collected from real-world events including the 2013 Boston Marathon bombings, the 2014 Ferguson unrest and the 2014 Ebola epidemic, and many other rumors reported on popular websites that document public rumors. The algorithm is able to predict the veracity of rumors with an accuracy of 75%. The ability to track rumors and predict their outcomes may have practical applications for news consumers, financial markets, journalists, and emergency services, and more generally to help minimize the impact of false information on Twitter.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>