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Detecting Spamming Groups in Social Media Based on Latent Graph

  • Qunyan Zhang
  • Chi Zhang
  • Peng CaiEmail author
  • Weining Qian
  • Aoying Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9093)

Abstract

Spammers in microblogging services aim to disseminate unuseful or misleading information, which leads to poor user experience and negative impact on the ecosystem of social media platform. Individual spammer detection, based on content and social network information, has been proposed to alleviate this predicament. However, most of the time spamming behavior is collaboratively conducted by a group of users, referred to as spamming group. In this paper, we propose to detect spamming groups in microblogging services. At the first step, we proposed RP-LDA to extract user features and find user groups within which users share similar retweeting behavior. Then, the degrees of individual users that are spammers are calculated by using a semi-supervised label propagation procedure. Finally, we determine the spamming groups using mixed membership distribution of users. Empirical studies over a real-life dataset demonstrate the effectiveness of our method and show that it can outperform the baseline.

Keywords

Spamming group Latent graph Social media 

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References

  1. 1.
    Benevenuto, F., Rodrigues, T., Magno, G., Almeida, V.A.F.: Detecting spammers on twitter. In: CEAS (2010)Google Scholar
  2. 2.
    Bíró, I., Siklósi, D., Szabó, J., Benczúr, A.A.: Linked latent dirichlet allocation in web spam filtering. In: AIRWeb, pp. 37–40 (2009)Google Scholar
  3. 3.
    Bíró, I., Szabó, J., Benczúr, A.A.: Latent dirichlet allocation in web spam filtering. In: AIRWeb, pp. 29–32 (2008)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Castillo, C., Donato, D., Becchetti, L., Boldi, P., Leonardi, S., Santini, M., Vigna, S.: A reference collection for web spam. SIGIR 40(2), 11–24 (2006)CrossRefGoogle Scholar
  6. 6.
    Chu, Z., Widjaja, I., Wang, H.: Detecting social spam campaigns on twitter. In: Bao, F., Samarati, P., Zhou, J. (eds.) ACNS 2012. LNCS, vol. 7341, pp. 455–472. Springer, Heidelberg (2012) CrossRefGoogle Scholar
  7. 7.
    Gao, H., Hu, J., Wilson, C., Li, Z., Chen, Y., Zhao, B. Y.: Detecting and characterizing social spam campaigns. In: CCS, pp. 681–683 (2010)Google Scholar
  8. 8.
    Ghosh, R., Surachawala, T., Lerman, K.: Entropy-based classification of ’retweeting’ activity on twitter (2011). CoRR, abs/1106.0346Google Scholar
  9. 9.
    Ghosh, S., Viswanath, B., Kooti, F., Sharma, N.K., Korlam, G., Benevenuto, F., Ganguly, N., Gummadi, P. K.: Understanding and combating link farming in the twitter social network. In: WWW, pp. 61–70 (2012)Google Scholar
  10. 10.
    Grier, C., Thomas, K., Paxson, V., Zhang, C. M.: @spam: the underground on 140 characters or less. In: CCS, pp. 27–37 (2010)Google Scholar
  11. 11.
    Henderson, K., Eliassi-Rad, T.: Applying latent dirichlet allocation to group discovery in large graphs. In: SAC, pp. 1456–1461 (2009)Google Scholar
  12. 12.
    Hu, X., Tang, J., Zhang, Y., Liu, H.: Social spammer detection in microblogging. In: IJCAI (2013)Google Scholar
  13. 13.
    Lee, K., Caverlee, J., Webb, S.: Uncovering social spammers: social honeypots + machine learning. In: SIGIR, pp. 435–442 (2010)Google Scholar
  14. 14.
    Li, F., Hsieh, M., An empirical study of clustering behavior of spammers and group-based anti-spam strategies. In: CEAS (2006)Google Scholar
  15. 15.
    Mukherjee, A., Liu, B., Glance, N.S.: Spotting fake reviewer groups in consumer reviews. In: WWW, pp. 191–200 (2012)Google Scholar
  16. 16.
    Mukherjee, A., Liu, B., Wang, J., Glance, N.S., Jindal, N.: Detecting group review spam. In: WWW, pp. 93–94 (2011)Google Scholar
  17. 17.
    Slaney, M., Casey, M.: Locality-sensitive hashing for finding nearest neighbors. IEEE, Signal Processing Magazine 25(2), 128–131 (2008). (lecture notes)CrossRefGoogle Scholar
  18. 18.
    Thomas, K., Grier, C., Ma, J., Paxson, V., Song, D.: Design and evaluation of a real-time URL spam filtering service. In: S&P, pp. 447–462 (2011)Google Scholar
  19. 19.
    Xia, F., Zhang, Q., Wang, C., Qian, W., Zhou, A.: On the rise and fall of sina weibo: Analysis based on a fixed user group. In: SSEPM (2015)Google Scholar
  20. 20.
    Xu, C., Zhang, J., Chang, K., Long, C.: Uncovering collusive spammers in chinese review websites. In: CIKM, pp. 979–988 (2013)Google Scholar
  21. 21.
    Yang, C., Harkreader, R.C., Zhang, J., Shin, S., Gu, G.: Analyzing spammers’ social networks for fun and profit: a case study of cyber criminal ecosystem on twitter. In: WWW, pp. 71–80 (2012)Google Scholar
  22. 22.
    Zhang, Q., Ma, H., Qian, W., Zhou, A.: Duplicate detection for identifying social spam in microblogs. In: BigData Congress, pp. 141–148 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Qunyan Zhang
    • 1
  • Chi Zhang
    • 1
  • Peng Cai
    • 1
    Email author
  • Weining Qian
    • 1
  • Aoying Zhou
    • 1
  1. 1.Institute for Data Science and Engineering, ECNU-PINGAN Innovative Research Center for BigDataEast China Normal UniversityShanghaiChina

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