Detection and Analysis of Water Army Groups on Virtual Community

  • Guirong ChenEmail author
  • Wandong Cai
  • Jiuming Huang
  • Huijie Xu
  • Rong Wang
  • Hua Jiang
  • Fengqin Zhang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 592)


Water army is prevalent in social networks and it causes harmful effect to the public opinion and security of cyberspace. This paper proposes a novel water army groups detection method which consists of 4 steps. Firstly, we break the virtual community into a series of time windows and find the suspicious periods when water army groups are active. Then we build the user cooperative networks of suspicious periods according to user’s reply behaviors and cluster them based on their Cosine similarity. After that, we prune the cooperative networks by just remaining the edges whose weight is larger than some threshold and get some suspicious user clusters. Finally, we conduct deeper analysis to the behaviors of the cluster users to determine whether they are water army groups or not. The experiment results show that our method can identify water army groups on virtual community efficiently and it has a high accuracy.


Social networks Detection of water army groups Empirical analysis of water army activities Virtual community 



This research is supported in part by the National Key Basic Research and Development Plan (Grant No. 2013CB329600), National Natural Science Foundation of China (Grant No. 71503260) and Natural Science Foundation of Shaanxi Province (Grant No. 2014JM8345).


  1. 1.
    Internet Network Information Center of China: The 33rd statistical report on Internet development of China [EB/OL]., 2014-03-05/2014-05-06 (in Chinese)
  2. 2.
    Fielding, N., Cobain, I.: Revealed: US Spy Operation that Manipulates Social Media, Guardian, 17 March 2011.
  3. 3.
    Chen, C., Wu, K., Srinivasan, V., et al.: Battling the internet water army: detection of hidden paid posters. In: International Conference on Advances in Social Networks Analysis and Mining, arXiv:1111.4297v1 [cs.SI], 18 November 2011
  4. 4.
    Fan, C., Xaio, X., Yu, L., et al.: Behavior analysis of network navy organization based on web forums. J. Shenyang Aerosp. Univ. 29(5), 64–67 (2010). (in Chinese)Google Scholar
  5. 5.
    Li, G., Gan, T., Kou, G.: Recognition of net-cheaters based on text sentiment analysis. Libr. Inf. 54(8), 77–80 (2010). (in Chinese)Google Scholar
  6. 6.
    Qin, M., Ke, Y.: Overview of web spammer detection. J. Softw. 25(7), 1505–1526 (2014). (in Chinese)Google Scholar
  7. 7.
    Jindal, N., Liu, B., Lim, E.P.: Finding unusual review patterns using unexpected rules. In: Huang, J., Koudas, N., Jones, G. (eds.) Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010), pp. 1549–1552. ACM Press, New York (2010)Google Scholar
  8. 8.
    Lim, E.P., Nguyen, V.A., Jindal, N., Liu, B., Lauw, H.W.: Detecting product review spammers using rating behaviors. In: Huang, J., Koudas, N., Jones, G., Wu, X., Collins-Thompson, K., An, A. (eds.) Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM 2010), pp. 939–948. ACM Press, New York (2010)Google Scholar
  9. 9.
    Wang, G., Xie, S., Liu, B., Yu, P.S.: Identify online store review spammers via social review graph. ACM Trans. Intell. Syst. Technol. (TIST) 3(4), 61 (2012)Google Scholar
  10. 10.
    Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Mille, A., Gandon, F., Misselis, J., Rabinovich, M., Staab, S. (eds.) Proceedings of the 21st International Conference on World Wide Web (WWW 2012), pp.191–200. ACM Press, New York (2012)Google Scholar
  11. 11.
    Liu, B.W., Yu, Y.T.: Web Data Mining. Tsinghua University Publication, Beijing (2013). (in Chinese)Google Scholar
  12. 12.
    Husna, H., Phithakkitnukoon, S., Palla, S., Dantu, R.: Behavior analysis of spam botnets. In: Proceedings of the 3rd International Conference on Communication Systems Software and Middleware and Workshops (COMSWARE 2008), pp. 246–253. IEEE Computer Society, Washington (2008)Google Scholar
  13. 13.
    Brendel, R., Krawczyk, H.: Application of social relation graphs for early detection of transient spammers. WSEAS Trans. Inf. Sci. Appl. 5(3), 267–276 (2008)Google Scholar
  14. 14.
    Zhou, Y.-B., Zhou, T.: A robust ranking algorithm to spamming. EPL (Europhys. Lett.) 94(4), 48002 (2011)CrossRefGoogle Scholar
  15. 15.
    Xu, C., Zhang, J., Chang, K., Long, C.: Uncovering collusive spammers in Chinese review websites. In: He, Q., Iyengar, A., Nejdl, W. (eds.) Proceedings of the 22nd ACM Conference on Information and Knowledge Management (CIKM 2013), pp. 979–988. ACM Press, New York (2013)Google Scholar
  16. 16.
    Lu, Y., Zhang, L., Xiao, Y., Li, Y.: Simultaneously detecting fake reviews and review spammers using factor graph model. In: Davis, H.C., Halpin, H., Pentland, A. (eds.) Proceedings of the 5th Annual ACM Web Science Conference (WebSci 2013), pp. 225–233. ACM Press, New York (2013)Google Scholar
  17. 17.
    Lin, C., Zhou, Y., Chen, K., He, J., Yang, X., Song, L.: Analysis and identification of spamming behaviors in Sina Weibo microblog. In: Zhu, F., He, Q., Yan, R. (eds.) Proceedings of the 7th Workshop on Social Network Mining and Analysis (SNAKDD 2013), pp. 5–13. ACM Press, New York (2013)Google Scholar
  18. 18.
    Bu, Z., Xia, Z., Wang, J.: A sock puppet detection algorithm on virtual spaces. Knowledge Based Systems 37, 366–377 (2013)CrossRefGoogle Scholar
  19. 19.
    Zheng, X., Lai, Y.M., Chow, K.P., et al.: Sockpuppet detection in online discussion forums. In: 2011 Seventh International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 374–377 (2011)Google Scholar
  20. 20.
    Jiang, F., Du, J., Sui, Y., Cao, C.: Outlier detection based on boundary and distance. Acta Electronica Sinica 38(3), 700–705 (2010). (in Chinese)Google Scholar
  21. 21.
    Lin, Y., Wang, X., Zhou, A.: Survey on quality evaluation and control of online reviews. J. Softw. 25(3), 506–527 (2014). (in Chinese)Google Scholar
  22. 22.
    Chen, G., Cai, W., Xu, H., et al.: Empirical analysis on human behavior dynamics in online forum. J. Hunan Univ. 40(11), 153–160 (2013). (in Chinese)Google Scholar
  23. 23.
    Peng, D., Cai, W.: The web forum crawling technology and system implementation. Comput. Eng. Sci. 44(1), 157–160 (2011)Google Scholar
  24. 24.
    Barabási, A.L.: The origin of bursts and heavy tails in human dynamics [J]. Nature 435(7039), 207–211 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Guirong Chen
    • 1
    • 2
    Email author
  • Wandong Cai
    • 1
  • Jiuming Huang
    • 3
  • Huijie Xu
    • 1
  • Rong Wang
    • 2
  • Hua Jiang
    • 2
  • Fengqin Zhang
    • 2
  1. 1.Department of Computer ScienceNorthwestern Polytechnical UniversityXi’anChina
  2. 2.School of Information and NavigationAir Force Engineering University of PLXi’anChina
  3. 3.College of ComputerNational University of Defense TechnologyChangshaChina

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