Advertisement

Detecting Rumors Through Modeling Information Propagation Networks in a Social Media Environment

  • Yang Liu
  • Songhua Xu
  • Georgia Tourassi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9021)

Abstract

In the midst of today’s pervasive influence of social media content and activities, information credibility has increasingly become a major issue. Accordingly, identifying false information, e.g. rumors circulated in social media environments, attracts expanding research attention and growing interests. Many previous studies have exploited user-independent features for rumor detection. These prior investigations uniformly treat all users relevant to the propagation of a social media message as instances of a generic entity. Such a modeling approach usually adopts a homogeneous network to represent all users, the practice of which ignores the variety across an entire user population in a social media environment. Recognizing this limitation of modeling methodologies, this study explores user-specific features in a social media environment for rumor detection. The new approach hypothesizes that whether a user tends to spread a rumor is dependent upon specific attributes of the user in addition to content characteristics of the message itself. Under this hypothesis, information propagation patterns of rumors versus those of credible messages in a social media environment are systematically differentiable. To explore and exploit this hypothesis, we develop a new information propagation model based on a heterogeneous user representation for rumor recognition. The new approach is capable of differentiating rumors from credible messages through observing distinctions in their respective propagation patterns in social media. Experimental results show that the new information propagation model based on heterogeneous user representation can effectively distinguish rumors from credible social media content.

Keywords

Rumor detection Heterogeneous user representation and modeling Information propagation model Information credibility in social media 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Yang, F., Liu, Y., Yu, X., Yang, M.: Automatic detection of rumor on sina weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, pp. 13:1–13:7. ACM (2012)Google Scholar
  2. 2.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684. ACM (2011)Google Scholar
  3. 3.
    Qazvinian, V., Rosengren, E., Radev, D.R., Mei, Q.Z.: Rumor has it: Identifying misinformation in microblogs. In: Proceedings of the Conference on Empirical Methods in Natural Language Processing, pp. 1589–1599. Association for Computational Linguistics (2011)Google Scholar
  4. 4.
    Moreno, Y., Nekovee, M., Pacheco, A.F.: Dynamics of rumor spreading in complex networks. Physical Review E 69(6), 066130 (2004)CrossRefGoogle Scholar
  5. 5.
    Xia, Z., Huang, L.L.: Emergence of social rumor: Modeling, analysis, and simulations. In: Proceedings of the 7th International Conference on Computational Science, pp. 90–97. Springer (2007)Google Scholar
  6. 6.
    Suh, B., Hong, L., Pirolli, P., Chi, E.H.: Want to be retweeted? large scale analytics on factors impacting retweet in twitter network. In: Proceedings of the 2010 IEEE Second International Conference on Social Computing (SocialCom), pp. 177–184. IEEE (2010)Google Scholar
  7. 7.
    Jin, F., Dougherty, E., Saraf, P., Cao, Y., Ramakrishnan, N: Epidemiological modeling of news and rumors on twitter. In: Proceedings of the 7th Workshop on Social Network Mining and Analysis, pp. 8:1–8:9. ACM (2013)Google Scholar
  8. 8.
    Sun, S., Liu, H., He, J., Du, X.: Detecting event rumors on sina weibo automatically. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds.) APWeb 2013. LNCS, vol. 7808, pp. 120–131. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  9. 9.
    Liao, Q.Y., Shi, L.: She gets a sports car from our donation: rumor transmission in a chinese microblogging community. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work, pp. 587–598. ACM (2013)Google Scholar
  10. 10.
    Lei, K., Zhang, K., Xu, K.: Understanding sina weibo online social network: A community approach. In: Proceedings of the 2013 IEEE Global Communications Conference (GLOBECOM), pp. 3114–3119. IEEE (2013)Google Scholar
  11. 11.
    Cai, G., Wu, H., Lv, R.: Rumors detection in chinese via crowd responses. In: Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 912–917 (2014)Google Scholar
  12. 12.
    Bao, Y.Y., Yi, C.Q., Xue, Y.B., Dong, Y.F.: A new rumor propagation model and control strategy on social networks. In: Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 1472–1473 (2013)Google Scholar
  13. 13.
    Liu, D.C., Chen, X.: Rumor propagation in online social networks like twitter-a simulation study. In: Proceedings of the Third International Conference on Multimedia Information Networking and Security (MINES), pp. 278–282. IEEE (2011)Google Scholar
  14. 14.
    Weibo Rumor Busting. http://weibo.com/weibopiyao
  15. 15.
    Weibo Hot Topics. http://d.weibo.com

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Yang Liu
    • 1
  • Songhua Xu
    • 1
  • Georgia Tourassi
    • 2
  1. 1.New Jersey Insititute of TechnologyUniversity Heights, NewarkUSA
  2. 2.Health Data Sciences Institute, Oak Ridge National LaboratoryBiomedical Science and Engineering CenterOak RidgeUSA

Personalised recommendations