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GSLDA: LDA-based group spamming detection in product reviews

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Abstract

Online product reviews are becoming increasingly important due to their guidance function in people’s purchase decisions. As being highly subjective, online reviews are subject to opinion spamming, i.e., fraudsters write fake reviews or give unfair ratings to promote or demote target products. Although there have been much efforts in this field, the problem is still left open due to the difficulties in gathering ground-truth data. As more and more people are using Internet in everyday life, group review spamming, which involves a group of fraudsters writing hype-reviews (promote) or defaming-reviews (demote) for one or more target products, becomes the main form of review spamming. In this paper, we propose a LDA-based computing framework, namely GSLDA, for group spamming detection in product review data. As a completely unsupervised approach, GSLDA works in two phases. It first adapts LDA (Latent Dirichlet Allocation) to the product review context in order to bound the closely related group spammers into a small-sized reviewer cluster, and then it extracts high suspicious reviewer groups from each LDA-clusters. Experiments on three real-world datasets show that GSLDA can detect high quality spammer groups, outperforming many state-of-the-art baselines in terms of accuracy.

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References

  1. Akoglu L, Chandy R, Faloutsos C (2013) Opinion fraud detection in online reviews by network effects. In: Proceedings of the seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, July 8-11, 2013

  2. Allahbakhsh M, Ignjatovic A, Benatallah B, Beheshti S-M-R, Bertino E, Foo N (2013) Collusion detection in online rating systems. In: Web Technologies and Applications, vol. 7808 of Lecture Notes in Computer Science. Springer, Berlin, pp 196–207

  3. Blei DM, Ng AY, Jordan MI (2003) Latent dirichlet allocation. J Mach Learn Res 3:993–1022

    MATH  Google Scholar 

  4. Choo E, Yu T, Chi M (2015) Detecting opinion spammer groups through community discovery and sentiment analysis. In: Proceedings of the 29th Annual IFIP WG 11.3 Working Conference on Data and Applications Security and Privacy XXIX, DBSec 2015, Fairfax, VA, USA, July 13-15, 2015, pp 170–187

  5. Crawford M, Khoshgoftaar TM, Prusa JD, Richter AN, Al Najada H (2015) Survey of review spam detection using machine learning techniques. J Big Data 2(1):23

    Article  Google Scholar 

  6. Fei G, Mukherjee A, Liu B, Hsu M, Castellanos M, Ghosh R (2013) Exploiting burstiness in reviews for review spammer detection. In: Seventh international AAAI conference on weblogs and social media

  7. Jindal N, Liu B (2008) Opinion spam and analysis. In: Proceedings of the 2008 International Conference on Web Search and Data Mining (New York, NY, USA). ACM, pp 219–230

  8. Lee KD, Han K, Myaeng S (2016) Capturing word choice patterns with LDA for fake review detection in sentiment analysis. In: Proceedings of the 6th International Conference on Web Intelligence, Mining and Semantics, WIMS 2016, Nîmes, France, June 13-15, 2016, pp 9:1–9:7

  9. Li J, Cardie C, Li S (2013) Topicspam: a topic-model based approach for spam detection. In: Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, ACL 2013, 4-9 August 2013, Sofia, Bulgaria, Volume 2: Short Papers, pp 217–221

  10. Li J, Ott M, Cardie C, Hovy E (2014) Towards a general rule for identifying deceptive opinion spam. In: Proceedings of the 52nd annual meeting of the association for computational linguistics (Volume 1: Long Papers) (Baltimore, Maryland), pp 1566–1576

  11. Lim E-P, Nguyen V-A, Jindal N, Liu B, Lauw HW (2010) Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (New York, NY, USA), CIKM’10, pp 939–948

  12. Mukherjee A, Kumar A, Liu B, Wang J, Hsu M, Castellanos M, Ghosh R (2013) Spotting opinion spammers using behavioral footprints. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York, NY, USA), KDD’13, pp 632–640

  13. Mukherjee A, Liu B, Glance N (2012) Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web (New York, NY, USA). ACM, pp 191–200

  14. Mukherjee A, Venkataraman V, Liu B, Glance NS (2013) What yelp fake review filter might be doing?. In: Proceedings of the Seventh International Conference on Weblogs and Social Media, ICWSM 2013, Cambridge, Massachusetts, USA, July 8–11, 2013

  15. Ott M, Choi Y, Cardie C, Hancock JT (2011) Finding deceptive opinion spam by any stretch of the imagination. In: Proceedings of the 49th annual meeting of the association for computational linguistics: Human language technologies - Volume 1 (Stroudsburg, PA, USA), pp 309–319

  16. Rayana S, Akoglu L (2015) Collective opinion spam detection: Bridging review networks and metadata. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Sydney, NSW, Australia, August 10-13, 2015, pp 985–994

  17. Sandulescu V, Ester M (2015) Detecting singleton review spammers using semantic similarity. In: Proceedings of the 24th International Conference on World Wide Web Companion, WWW 2015, Florence, Italy, May 18-22, 2015 - Companion Volume, pp 971–976

  18. Viviani M, Pasi G (2017) Credibility in social media: opinions, news, and health information - a survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 7(5):e1209

    Google Scholar 

  19. Wang G, Xie S, Liu B, Yu PS (2011) Review graph based online store review spammer detection. In: ICDM, pp 1242–1247

  20. Wang Z, Gu S, Zhao X, Xu X (2017) Graph-based review spammer group detection. Knowledge and Information Systems

  21. Wang Z, Hou T, Song D, Li Z, Kong T (2016) Detecting review spammer groups via bipartite graph projection. Comput J 59(6):861–874

    Article  Google Scholar 

  22. Xu C, Zhang J (2015) Towards collusive fraud detection in online reviews. In: 2015 IEEE International Conference on Data Mining, ICDM 2015, Atlantic City, NJ, USA, November 14-17, 2015, pp 1051–1056

  23. Xu C, Zhang J, Chang K, Long C (2013) Uncovering collusive spammers in chinese review websites. In: Proceedings of the 22Nd ACM International Conference on Conference on Information & Knowledge Management (New York, NY, USA). ACM, pp 979–988

  24. Xu X, Yuruk N, Feng Z, Schweiger TAJ (2007) SCAN: a structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Jose, California, USA, August 12-15, 2007, pp 824–833

  25. Ye J, Akoglu L (2015) Discovering opinion spammer groups by network footprints. In: Appice A, Rodrigues PP, Santos Costa V, Soares C, Gama J, Jorge A (eds) Machine Learning and Knowledge Discovery in Databases, Volume 9284 of Lecture Notes in Computer Science. Springer International Publishing, pp 267–282

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Authors and Affiliations

  1. Shenyang Ligong University, Shenyang, China

    Zhuo Wang & Songmin Gu

  2. University of Arkansas at Little Rock, Little Rock, AR, 72204, USA

    Xiaowei Xu

Authors
  1. Zhuo Wang
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  2. Songmin Gu
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  3. Xiaowei Xu
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Correspondence to Zhuo Wang.

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Wang, Z., Gu, S. & Xu, X. GSLDA: LDA-based group spamming detection in product reviews. Appl Intell 48, 3094–3107 (2018). https://doi.org/10.1007/s10489-018-1142-1

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