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Applying a tendency to be well retweeted to false information detection

Published:28 November 2016Publication History

ABSTRACT

While a lot of useful information can be found in SNS, false information also diffuses through it, thereby confusing many people sometimes. In this paper, we predict a tendency of tweets to be well retweeted and consider applying the tendency to false information detection. The tendency prediction can be implemented with simple features of tweets. We examine the effect of the tendency when it is used in false information detection empirically. Our experimental results indicate that it would be valuable to take the tendency into account for the detection. We also discuss findings when applying them to tweets in Japanese.

References

  1. E. F. Can, H. Oktay, and R. Manmatha. Predicting retweet count using visual cues. In Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, CIKM '13, pages 1481--1484, New York, NY, USA, 2013. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. C. Castillo, M. Mendoza, and B. Poblete. Information credibility on twitter. In Proceedings of the 20th International Conference on World Wide Web, WWW '11, pages 675--684, New York, NY, USA, 2011. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. A. Gupta and P. Kumaraguru. Credibility ranking of tweets during high impact events. In Proceedings of the 1st Workshop on Privacy and Security in Online Social Media, PSOSM '12, pages 2:2--2:8, New York, NY, USA, 2012. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. A. Gupta, H. Lamba, and P. Kumaraguru. $1.00 per rt #bostonmarathon #prayforboston: analyzing fake content on twitter. In eCrime Research Summit. http://precog.iiitd.edu.in/Publications_files/ecrs2013_ag_hl_pk.pdf, Sept. 2013.Google ScholarGoogle Scholar
  5. J. Ma, W. Gao, Z. Wei, Y. Lu, and K.-F. Wong. Detect rumors using time series of social context information on microblogging websites. In Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, CIKM '15, pages 1751--1754, New York, NY, USA, 2015. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. M. Mathioudakis and N. Koudas. Twittermonitor: Trend detection over the twitter stream. In Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data, SIGMOD '10, pages 1155--1158, New York, NY, USA, 2010. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Nadamoto, M. Miyabe, and E. Aramaki. Analysis of microblog rumors and correction texts for disaster situations. In Proceedings of International Conference on Information Integration and Web-based Applications & Services, IIWAS '13, pages 44:44--44:52, New York, NY, USA, 2013. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. A. Umejima, M. Miyabe, E. Aramaki, and A. Nadamoto. Tendency of rumor and correction re-tweet on the twitter during disasters (in Japanese). Technical Report http://id.nii.ac.jp/1001/00075459/, IPSJ SIG Technical Reports, July 2011.Google ScholarGoogle Scholar
  9. Y. Yasuda. Information dissemination in social media: Hubs and demagogues (in Japanese). Technical Report http://hdl.handle.net/10112/8399, Kansai University, Dec. 2013.Google ScholarGoogle Scholar
  10. Z. Yoshida and M. Aritsugi. Rumor detection using tendencies to be well retweeted (in Japanese). Technical Report http://db-event.jpn.org/deim2016/papers/351.pdf, DEIM Forum, Mar. 2016.Google ScholarGoogle Scholar
  11. Y. Yoshitsugu. Roles of social media at the time of major disasters observed in the great east japan earthquake: Twitter as an example (in Japanese). Technical Report http://www.nhk.or.jp/bunken/english/reports/summary/201107/02.html, NHK Broadcasting Culture Research Institute, July 2011.Google ScholarGoogle Scholar
  12. Z. Zhao, P. Resnick, and Q. Mei. Enquiring minds: Early detection of rumors in social media from enquiry posts. In Proceedings of the 24th International Conference on World Wide Web, WWW '15, pages 1395--1405, New York, NY, USA, 2015. ACM. Google ScholarGoogle ScholarDigital LibraryDigital Library

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                  iiWAS '16: Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services
                  November 2016
                  528 pages
                  ISBN:9781450348072
                  DOI:10.1145/3011141

                  Copyright © 2016 ACM

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                  New York, NY, United States

                  Publication History

                  • Published: 28 November 2016

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