An Analysis of Credibility of Content on Twitter
Dr. Bhoomi Gupta, Maharaja Agrasen Institute of Technology; Shivani Shukla ,Maharaja Agrasen Institute of Technology
Twitter, Credibility, Online Social Media
We analyze the information credibility of news propagated through Twitter, a popular microblogging service. Previous research has shown that most of the messages posted on Twitter are truthful, but the service is also used to spread misinformation and false rumors, often unintentionally. Specifically, we analyze microblog postings related to “trending” topics, and classify them as credible or not credible, based on features extracted from them. We use features from users’ posting and re-posting (“re-tweeting”) behavior, from the text of the posts, and from citations to external sources. Our results show that there are measurable differences in the way messages propagate, that can be used to classify them automatically as credible or not credible.
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[1] E. Agichtein, C. Castillo, D. Donato, A. Gionis, andG. Mishne. Finding high-quality content in social media. In WSDM ’08: Proceedings of the international conference on Web search and web data mining, pages 183–194, New York, NY, USA, 2008. ACM.
[2] Alonso, Omar, Carson, Chad, Gerster, David, Ji, Xiang,and Nabar, Shubha. Detecting Uninteresting Content in Text Streams. In SIGIR Crowdsourcing for Search
[3] J. Chen, R. Nairn, L. Nelson, M. Bernstein, and E. Chi.Short and tweet: experiments on recommending content from information streams. CHI '10, pages 1185-1194, 2010.
[4] C. R. W. Watch. Leap of faith: Using the internet despite the dangers.http://www.consumerwebwatch.org/pdfs/princeton.pdf, October 2005.
[5] S. Chhabra, A. Aggarwal, F. Benevenuto, and P. Kumaraguru. Phi.sh/$ocial: the phishing landscape through short urls. CEAS 2011, pages 92-101, 2011.Evaluation Workshop, 2010.
[6] T. J. Johnson, B. K. Kaye, S. L. Bichard, and W. J. Wong. Every blog has its day: Politically-interested internet users’ perceptions of blog credibility. Journal of Computer-Mediated Communication, 13(1), 2007.
[7] A. Java, X. Song, T. Finin, and B. Tseng. Why we twitter: understanding microblogging usage and communities. In WebKDD/SNA-KDD ’07: Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis, pages 56–65, New York, NY, USA,2007. ACM.
[8] Pear Analytics. Twitter study. http://www.pearanalytics.com/wp-content/uploads/2009/- 08/Twitter-Study-August-2009.pdf, August 2009.
[9] M. Naaman, J. Boase, and C. H. Lai. Is it really about me?: message content in social awareness streams. In Proceedings of the 2010 ACM conference on Computer supported cooperative work, CSCW ’10, pages 189–192, New York, NY, USA, 2010. ACM.
[10] H. Kwak, C. Lee, H. Park, and S. Moon. What is twitter, a social network or a news media? In World Wide Web Conference. ACM Press, 2010.
[11] V. Lampos, T. D. Bie, and N. Cristianini. Flu detector - tracking epidemics on twitter. In European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD 2010), pages 599–602, Barcelona, Spain, 2010. Springer, Springer.
[12] J. Sankaranarayanan, H. Samet, B. E. Teitler, M. D. Lieberman, and J. Sperling. TwitterStand: news in tweets.In GIS ’09: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pages 42–51, New York, NY, USA,November 2009. ACM Press.
[13] T. Sakaki, M. Okazaki, and Y. Matsuo. Earthquake shakes Twitter users: real-time event detection by social sensors. In Proceedings of the 19th international conference on World Wide Web, WWW ’10, pages 851–860, New York, NY, USA, April 2010. ACM.
[14] A. M. Popescu and M. Pennacchiotti. Detecting Controversial events from twitter. In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM ’10, pages 1873–1876, NewYork, NY, USA, 2010. ACM.
[15] M. Mathioudakis and N. Koudas. TwitterMonitor: trend detection over the twitter stream. In Proceedings of the 2010 international conference on Management of data, pages 1155–1158. ACM, 2010.
[16] A. L. Hughes and L. Palen. Twitter adoption and use in mass convergence and emergency events. In ISCRAM Conference, May 2009.
[17] K. Poulsen. Firsthand reports from california wildfires pour through twitter. October 2007.
[18] C. Castillo, M. Mendoza, and B. Poblete. Information Credibility on Twitter. In WWW, pages 675{684, 2011.
[19] K. R. Canini, B. Suh, and P. L. Pirolli. Finding credible information sources in social networks based on content and social structure. In SocialCom, 2011.
[20] J. Ratkiewicz, M. Conover, M. Meiss, B. Gon_calves, S. Patil, A. Flammini, and F. Menczer. Truthy: mapping the spread of astroturf in microblog streams. WWW '11.
Paper ID: GRDJEV02I050064
Published in: Volume : 2, Issue : 5
Publication Date: 2017-05-01
Page(s): 98 - 101
Published in: Volume : 2, Issue : 5
Publication Date: 2017-05-01
Page(s): 98 - 101