Gatekeeping Behavior Analysis for Information Credibility Assessment on Weibo

  • Bailin Xie
  • Yu WangEmail author
  • Chao Chen
  • Yang Xiang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9955)


Microblogging sites, such as Sina Weibo and Twitter, have gained significantly in popularity and become an important source for real-time information dissemination. Inevitably, these services are also used to spread false rumors and misinformation, usually with the unintentional collaboration from innocent users. Previous studies show that microblog information credibility can be assessed automatically based on the features extracted from message contents and users. In this paper, we address this problem from a new perspective by exploring the human input in the propagation process of popular microblog posts. Specifically, we consider that the users are the gatekeepers of their own media portal on microblogging sites, as they decide which information is filtered for dissemination to their followers. We find that truthful posts and false rumors exhibit distinguishable patterns in terms of which gatekeepers forward them and what the gatekeepers comment on them. Based on this finding, we propose to assess the information credibility of popular microblog posts with Hidden Markov Models (HMMs) of gatekeeping behavior. The proposed approach is evaluated using a real life data set that consists of over ten thousand popular posts collected from Sina Weibo.


Online social networks Microblogging Weibo Information credibility HMM 



This work is supported by the Training Program for Outstanding Young Teachers in University of Guangdong Province under grant No. GWTPSY201403.


  1. 1.
  2. 2.
    Sina Weibo.
  3. 3.
    Kwak, H., Lee, C., Park, H., Moon, S.: What is Twitter, a social network or a news media? In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 591–600. ACM, New York (2010)Google Scholar
  4. 4.
    RT News. The Tweet that rocked Wall Street: $200 billion lost on fake message. 24 April 2013.
  5. 5.
    Pash, C.: The lure of naked hollywood star photos sent the internet into meltdown in New Zealand. Business Insider, 7 September 2014.
  6. 6.
    Al-Khalifa, H.S., Al-Eidan, R.M.: An experimental system for measuring the credibility of news content in Twitter. Int. J. Web Inf. Syst. 7(2), 130–151 (2011)CrossRefGoogle Scholar
  7. 7.
    Suzuki, Y., Nadamoto, A.: Credibility assessment using Wikipedia for messages on social network services. In: The Ninth International Conference on Dependable, Autonomic and Secure Computing, pp. 887–894 (2011)Google Scholar
  8. 8.
  9. 9.
    Liang, C., Liu, Z., Sun, M.: Expert finding for microblog misinformation identification. In: COLING (Posters), pp. 703–712 (2012)Google Scholar
  10. 10.
    Castillo, C., Mendoza, M., Poblete, B.: Information credibility on Twitter. In: Proceedings of the 20th International Conference on World Wide Web, pp. 675–684 (2011)Google Scholar
  11. 11.
    Gupta, M., Zhao, P., Han, J.: Evaluating event credibility on Twitter (2012).
  12. 12.
    Xia, X., Yang, X., Wu, C., Li, S., Bao, L.: Information credibility on Twitter in emergency situation. In: Chau, M., Wang, G., Yue, W.T., Chen, H. (eds.) PAISI 2012. LNCS, vol. 7299, pp. 45–59. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  13. 13.
    Yang, F., Yu, X.: Automatic detection of rumor on Sina Weibo. In: Proceedings of the ACM SIGKDD Workshop on Mining Data Semantics, pp. 1–7 (2012)Google Scholar
  14. 14.
    Grier, C., Thomas, K., Paxson, V., Zhang, M.: @spam: the underground on 140 characters or less. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS 10, pp. 27–37. ACM, New York (2010)Google Scholar
  15. 15.
    Thomas, K., Grier, C., Song, D., Paxson, V.: Suspended accounts in retrospect: an analysis of Twitter spam. In: Proceedings of the 2011 ACM SIGCOMM Conference on Internet Measurement Conference, IMC 2011, pp. 243–258. ACM, New York (2011)Google Scholar
  16. 16.
    Lee, S., Kim, J.: Warningbird: a near real-time detection system for suspicious URLs in Twitter stream. IEEE Trans. Dependable Secure Comput. 10(3), 183–195 (2013)CrossRefGoogle Scholar
  17. 17.
    Oliver, J., Pajares, P., Ke, C., Chen, C., Xiang, Y.: An in-depth analysis of abuse on twitter. Technical report, Trend Micro, 225 E. John Carpenter Freeway, Suite 1500 Irving, Texas 75062 U.S.A., September 2014Google Scholar
  18. 18.
    Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)CrossRefGoogle Scholar
  19. 19.
    Wei, W., Shi-Bin, X.: Sentiment analysis of Chinese microblog based on multi-feature and combined classification. J. Beijing Inf. Sci. Technol. Univ. 28(4), 39–45 (2013). (in Chinese)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Cisco School of InformaticsGuangdong University of Foreign StudiesGuangzhouChina
  2. 2.School of Information TechnologyDeakin UniversityGeelongAustralia

Personalised recommendations