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Vote-based integration of review spam detection algorithms

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Abstract

Due to the growth of online review data, detecting fake or fraudulent reviews is becoming an urgent issue. One barrier to effective detection of fake reviews/reviewers is the great difficulty of collecting ground-truth data—fake reviews are hard to judge, even by human experts. As researchers propose a large number of methods to detect review spam from a variety of perspectives, e.g., text-based or behavior-based, there is a need to combine these methods to improve the overall detection performance. In this paper, we raise the important question of how to integrate multiple ranking lists generated by different types of review spam detection algorithms into an overall ranking list. To address this problem, we propose a novel unsupervised integration model, namely SpamVote, that combines multiple ranking lists together by voting. In view of the diversity of review spam strategies, we model the fitness of a particular algorithm to detect a specific item as latent, and learn the latent variables from the ranking data. Extensive experiments on real-world datasets with various kinds of algorithms show that the integrated ranking list created by SpamVote outperforms the voting lists with a large probability.

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Notes

  1. https://brilliant.org/wiki/rearrangement-inequality/

  2. https://github.com/zhuowangsylu/SpamVote

References

  1. Luca M, Zervas G (2016) Fake it till you make it: reputation, competition, and yelp review fraud. Manag Sci 62(12):3412–3427

    Article  Google Scholar 

  2. 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 - vol 1. Stroudsburg, PA, USA, pp 309–319

  3. 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, 8-11 July

  4. Jindal N, Liu B (2008) Opinion spam and analysis. In: Proceedings of the 2008 international conference on Web Search and data mining. ACM, pp 219–230

  5. 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. ACM, pp 191–200

  6. 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,10-13 August, pp 985–994

  7. Kumar N, Venugopal D, Qiu L, Kumar S (2019) Detecting anomalous online reviewers: an unsupervised approach using mixture models. J Manag Inf Syst 36(4):1313–1346

    Article  Google Scholar 

  8. Dou Y, Ma G, Yu PS, Xie S (2020) Robust spammer detection by nash reinforcement learning. In: Gupta R, Liu Y, Tang J, Prakash BA (eds) KDD ’20: the 26th ACM SIGKDD conference On knowledge discovery and data mining, virtual event, CA, USA, 23-27 August 2020. ACM, pp 924–933

  9. Wang G, Xie S, Liu B, Yu PS (2011) Review graph based online store review spammer detection. In: 11Th IEEE International Conference On Data Mining, ICDM 2011, Vancouver, BC, Canada, 11-14 December 2011, pp 1242–1247

  10. 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 

  11. Rastogi A, Mehrotra M (2017) Opinion spam detection in online reviews. JIKM 16(4):1–38

    Google Scholar 

  12. Li J, Wang X, Yang L, Zhang P, Yang D (2020) Identifying ground truth in opinion spam: an empirical survey based on review psychology. Appl Intell 50(11):3554–3569

    Article  Google Scholar 

  13. Pourhabibi T, Ong K, Kam B, Boo YL (2020) Fraud detection: a systematic literature review of graph-based anomaly detection approaches. Decis Support Syst 133:113303

    Article  Google Scholar 

  14. 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. CIKM ’10, New York, NY, USA pp 939–948

  15. 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. KDD ’13, New York, NY, USA pp 632–640

  16. Choo E, Yu T, Chi M (2015) Detecting opinion spammer groups through community discovery and sentiment analysis. In: Data and applications security and privacy XXIX - 29th annual IFIP WG 11.3 working conference, DBSec 2015, fairfax, VA, USA, 13-15 July 2015, Proceedings, pp 170–187

  17. Kaghazgaran P, Caverlee J, Squicciarini AC (2018) Combating crowdsourced review manipulators: a neighborhood-based approach. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, WSDM 2018, Marina Del Rey, CA, USA, 5-9 February 2018, pp 306–314

  18. Wang Z, Hu R, Chen Q, Gao P, Xu X (2020) Collueagle: collusive review spammer detection using markov random fields. Data Min Knowl Discov 34(6):1621–1641

    Article  Google Scholar 

  19. Fang Y, Wang H, Zhao L, Yu F, Wang C (2020) Dynamic knowledge graph based fake-review detection. Appl Intell 50(12):4281–4295

    Article  Google Scholar 

  20. Wei F, Li W, Liu S (2010) Irank: a rank-learn-combine framework for unsupervised ensemble ranking. J Assoc Inf Sci Technol 61(6):1232–1243

    Google Scholar 

  21. Lebanon G, Lafferty JD (2002) Cranking: combining rankings using conditional probability models on permutations. In: Sammut C, Hoffmann AG (eds) machine learning, proceedings of the nineteenth international conference (ICML 2002), University of New South Wales, Sydney, Australia, 8-12 July 2002, Morgan Kaufmann, pp 363–370

  22. Wang Z, Chen Q (2020) Monitoring online reviews for reputation fraud campaigns. Knowl Based Syst 195:105685

    Article  Google Scholar 

  23. 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, 8-11 July

  24. 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. https://doi.org/10.1093/comjnl/bxv068

    Article  Google Scholar 

  25. Wang Z, Gu S, Zhao X, Xu X (2018) Graph-based review spammer group detection. Knowl Inf Syst 55(3):571–597. https://doi.org/10.1007/s10115-017-1068-7

    Article  Google Scholar 

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Acknowledgements

We are very grateful to Knectt Lendoye from Beijing Institute of Technology for proofreading our manuscript, and Dr. Feng Wen from Shenyang Ligong University for the support of this research. We also thank the anonymous reviewers who gave invaluable suggestions on this work. This work was supported by the 2021 Shenyang Ligong University Research and Innovation Team Development Program Support Project under Grant No. SYLUTD202105.

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Authors and Affiliations

  1. School of Information Science and Engineering, Shenyang Ligong University, No.6, Nanping Rd., Shenyang, 110159, LiaoNing, China

    Zhuo Wang & Hui Li

  2. School of Computer, Northeastern University, No.195, Chuangxin Rd., Shenyang, 110159, LiaoNing, China

    Huiyan Wang

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  1. Zhuo Wang
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  2. Hui Li
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Correspondence to Zhuo Wang.

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Wang, Z., Li, H. & Wang, H. Vote-based integration of review spam detection algorithms. Appl Intell 53, 5048–5059 (2023). https://doi.org/10.1007/s10489-022-03807-7

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