Collusion-aware detection of review spammers in location based social networks

  • Jiuxin Cao
  • Rongqing Xia
  • Yifang Guo
  • Zhuo MaEmail author
Part of the following topical collections:
  1. Special Issue on Social Computing and Big Data Applications


To ensure the quality of online review, more and more location-based social networks (LBSNs), like Yelp, have established the filtering systems to detect groups of review spammers. This is not an easy task. Review spammers use camouflage methods to maintain their spam behavior in a very low density to try to conceal themselves in normal users. These camouflaged spammers, driven by profits, are hired by some stores to write fake reviews in groups so as to raise these stores or to belittle their competitors. To avoid the unhealthy competition, in this paper, we propose a novel detection mechanism to discern collusive review spammers, including individuals and groups. The key point of our mechanism is to identify hidden spammers through multiple anomalous relationship features, especially the collusive relation between review spammers and the business competition between locations. Based on multi-view anomalous features, two detection models are proposed for individual and group discovery, respectively. For malicious individuals, a detection model based on Markov Random Field (MRF) is constructed to formalize an inference problem, where the corresponding marginal distribution of users and locations are calculated respectively. For review spammer groups, a hierarchical agglomerative clustering algorithm is conceived according to a new validity index to make sure the collusion relation in each group is close at most. Experiment results show that our method can detect collusive spammers and groups more accurately and comprehensively over the current researches. The additional experiments also show the effectiveness of each anomalous feature in detecting review spammers.


Collusive review spammers Location based social network Anomalous features Markov random field 


  1. 1.
    Akoglu, L., Chandy, R., Faloutsos, C.: Opinion fraud detection in online reviews by network effects[J]. ICWSM. 13, 2–11 (2013)Google Scholar
  2. 2.
    Chan C L, Lai J, Hooi B, et al. The Message or the Messenger? Inferring Virality and Diffusion Structure from Online Petition Signature Data[C]// International Conference on Social Informatics. Springer, Cham 499–517 (2017)Google Scholar
  3. 3.
    D’onfro J.: A Whopping 20% of Yelp Reviews Are Fake[EB/OL]. (2013)
  4. 4.
    Feng S, Banerjee R, Choi Y. Syntactic stylometry for deception detection[C]//proceedings of the 50th annual meeting of the Association for Computational Linguistics: short papers-volume 2. Association for Computational Linguistics 171–175 (2012)Google Scholar
  5. 5.
    Feng S, Xing L, Gogar A, et al. Distributional footprints of deceptive product reviews[C]//seventh international AAAI conference onweblogs and social Media (2013)Google Scholar
  6. 6.
    Hooi B, Song H A, Beutel A, et al. FRAUDAR: Bounding Graph Fraud in the Face of Camouflage[C]// ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM 895–904 (2016)Google Scholar
  7. 7.
    Jindal N, Liu B. Opinion spam and analysis[C]//proceedings of the 2008 international conference on web search and data mining. ACM. 219–230 (2008)Google Scholar
  8. 8.
    KC S, Mukherjee A. On the temporal dynamics of opinion spamming: case studies on yelp[C]//proceedings of the 25th international conference on world wide web. International World Wide Web Conferences Steering Committee 369–379 (2016)Google Scholar
  9. 9.
    Kindermann, R., Snell, J.L.: Markov Random Fields and their Applications[M]. American Mathematical Society, Providence (1980)CrossRefzbMATHGoogle Scholar
  10. 10.
    Li, F., Huang, M., Yang, Y., et al.: Learning to identify review spam[C]//IJCAI proceedings-international joint conference on. Artif. Intell. 22(3), 2488 (2011)Google Scholar
  11. 11.
    Li H, Chen Z, Liu B, et al. Spotting fake reviews via collective positive-unlabeled learning[C]//data mining (ICDM), 2014 IEEE international conference on. IEEE 899–904 (2014)Google Scholar
  12. 12.
    Li H, Chen Z, Mukherjee A, et al. Analyzing and detecting opinion spam on a large-scale dataset via temporal and spatial patterns[C]//ninth international AAAI conference on web and social Media 634–637 (2015)Google Scholar
  13. 13.
    Liu S, Hooi B, Faloutsos C. HoloScope: topology-and-spike aware fraud detection[C]// ACM on conference on information and knowledge management. ACM 1539–1548 (2017)Google Scholar
  14. 14.
    Luca, M., Zervas, G.: Fake it till you make it: reputation, competition, and yelp review fraud[J]. Management science journal of the Institute for Operations Research & the management. Sciences. 62, 3412–3427 (2016)Google Scholar
  15. 15.
    Mukherjee A, Liu B, Glance N. Spotting fake reviewer groups in consumer reviews[C]//proceedings of the 21st international conference on world wide web. ACM 191–200 (2012)Google Scholar
  16. 16.
    Mukherjee A, Kumar A, Liu B, et al. Spotting opinion spammers using behavioral footprints[C]//proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining. ACM 632–640 (2013)Google Scholar
  17. 17.
    Mukherjee A, Venkataraman V, Liu B, et al. What yelp fake review filter might be doing?[C]//ICWSM. (2013)Google Scholar
  18. 18.
    Ott M, Choi Y, Cardie C, et al. Finding deceptive opinion spam by any stretch of the imagination[C]//proceedings of the 49th annual meeting of the Association for Computational Linguistics: human language technologies-volume 1. Association for Computational Linguistics 309–319 (2011)Google Scholar
  19. 19.
    Rahman, M., Carbunar, B., Ballesteros, J., Chau, D.H.P.: To catch a fake: curbing deceptive yelp ratings and venues[J]. Stat. Anal. Data Min. ASA Data Sci. J. 8(3), 147–161 (2015)MathSciNetCrossRefGoogle Scholar
  20. 20.
    Rayana S, Akoglu L. Collective opinion spam detection: bridging review networks and metadata[C]//Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining. ACM 985–994 (2015)Google Scholar
  21. 21.
    Rayana S, Akoglu L. Collective opinion spam detection using active inference[C]//proceedings of the 2016 SIAM international conference on data mining. Soc. Ind. Appl. Math. 630–638 (2016)Google Scholar
  22. 22.
    Shin K, Hooi B, Faloutsos C. M-zoom: fast dense-block detection in tensors with quality guarantees[C]// joint European conference on machine learning and knowledge discovery in databases. Springer International Publishing 264–280 (2016)Google Scholar
  23. 23.
    Wang G, Xie S, Liu B, et al. Review graph based online store review spammer detection[C]//data mining (ICDM), 2011 IEEE 11th international conference on. IEEE 1242–1247 (2011)Google Scholar
  24. 24.
    Xie S, Wang G, Lin S, et al. Review spam detection via temporal pattern discovery[C]//proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. ACM. 823–831 (2012)Google Scholar
  25. 25.
    Xu C, Zhang J. Towards collusive fraud detection in online reviews[C]//data mining (ICDM), 2015 IEEE international conference on. IEEE 1051–1056 (2015)Google Scholar
  26. 26.
    Xu C, Zhang J. Combating product review spam campaigns via multiple heterogeneous pairwise features[C]//Proceedings of the 2015 SIAM international conference on data mining. Soc. Ind. Appl. Math. 172–180 (2015)Google Scholar
  27. 27.
    Xu C, Zhang J, Chang K, et al. Uncovering collusive spammers in Chinese review websites[C]//proceedings of the 22nd ACM international conference on conference on information & knowledge management. ACM 979–988 (2013)Google Scholar
  28. 28.
    Ye J, Akoglu L. Discovering opinion spammer groups by network footprints[M]//machine learning and knowledge discovery in databases. Springer International Publishing 267–282 (2015)Google Scholar
  29. 29.
    Yedidia, J.S., Freeman, W.T., Weiss, Y.: Understanding belief propagation and its generalizations[J]. Explor. Artif. Intell. New Millenium. 54(1), 276–286 (2002)Google Scholar
  30. 30.
    Zheng Y.: Location-based social networks: users[M]. Computing with spatial trajectories. Springer New York, 243–276 (2011)Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityNanjingChina
  2. 2.Jiangsu Provincial Key Laboratory of Computer Networking Technology, School of Cyber Science and EngineeringSoutheast UniversityNanjingChina

Personalised recommendations