skip to main content
10.1145/3427423.3427434acmotherconferencesArticle/Chapter ViewAbstractPublication PagessietConference Proceedingsconference-collections
research-article

Detection of online review spam: a literature review

Published:28 December 2020Publication History

ABSTRACT

Online reviews have become an important resource for customers. It has become a habit for customers to first read a review before deciding to make a purchase. But it can be used by fraudsters to make review spam. This activity can result in the wrong customer purchase decision. Automatic opinion mining methods can also provide inaccurate conclusions due to this activity. This paper aims to provide a literature review on the online review spam detection topic. We identify papers relevant to related topics since 2015, understanding each paper to extract findings, similarities, and research gaps. We find that studies on this topic can be categorized into three focus groups. Focus on review spam detection methods, studies on individuals who write review spam, and studies that examine the spammer groups. Each focus of research has its strengths and weaknesses method which provide benefits in the field of review spam detection.

References

  1. Y. Lin, T. Zhu, H. Wu, J. Zhang, X. Wang, and A. Zhou, "Towards online anti-opinion spam: Spotting fake reviews from the review sequence," in 2014 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp. 261--264, 2014. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. N. N. Ho-Dac, S. J. Carson, and W. L. Moore, "The effects of positive and negative online customer reviews: Do brand strength and category maturity matter?," Journal of Marketing., vol. 77, no. 6, pp. 37--53, 2013.Google ScholarGoogle ScholarCross RefCross Ref
  3. Peñalver-Martinez. I, Garcia-Sanchez. F, Valencia-Garcia. R, Rodríguez-García. M. Á, Moreno. V, Fraga. A, and Sanchez-Cervantes. J, "Feature-based opinion mining through ontologies," Expert Systems with Applications., vol. 41, no. 13, pp.5995--6008, 2014.Google ScholarGoogle ScholarCross RefCross Ref
  4. A. Mukherjee, B. Liu, and N. Glance, "Spotting fake reviewer groups in consumer reviews," in Proceedings of the 21st international conference on World Wide Web, pp. 191--200, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. S. Shojaee, A. Azman, M. Murad, N. Sharef, and N. Sulaiman, "A Framework for Fake Review Annotation," in 17th UKSIM-AMSS International Conference on Modelling and Simulation, pp. 153--158, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. J. G. Thanikkal, and M. Danish, "A Novel Approach to Improve Spam Detection using SDS Algorithm.," International Journal for Innovative Research in Science & Technology., vol. 1, no. 12, pp. 306--310, 2016.Google ScholarGoogle Scholar
  7. Y. Xu, B. Shi, W. Tian, and W. Lam, "A Unified Model for Unsupervised Opinion Spamming Detection Incorporating Text Generality," in Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence, pp. 726--731, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. H. Li, Z. Chen, A. Mukherjee, B. Liu, and J. Shao, "Analyzing and Detecting Opinion Spam on a Large-Scale Dataset via Temporal and Spatial Patterns," in Proceedings of the Ninth International AAAI Conference on Web and Social Media, pp. 634--637, 2015.Google ScholarGoogle Scholar
  9. S. Rayana, and L. Akoglu, "Collective Opinion Spam Detection: Bridging Review Networks and Metadata," in Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 985--994, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. Kokate, and B. Tidke, "Fake Review and Brand Spam Detection using J48 Classifier," International Journal of Computer Science and Information Technologies., vol. 6, no. 4, pp. 3523--3526, 2015.Google ScholarGoogle Scholar
  11. A. Karam, and B. Zhou, "Online Review Spam Detection by New Linguistic Features," in iConference 2015 Proceedings, 2015.Google ScholarGoogle Scholar
  12. Y. Chen, and H. Chen, "Opinion Spam Detection in Web Forum: A Real Case Study," in International World Wide Web Conference Committee (IW3C2), pp. 173--183, 2015. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. S. Rayana, and L. Akoglu, "Collective Opinion Spam Detection using Active Inference," in Society for Industrial and Applied Mathematics, pp. 630--638, 2016.Google ScholarGoogle Scholar
  14. Y. Ren, and Y. Zhang, "Deceptive Opinion Spam Detection Using Neural Network," in Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pp. 140--150, 2016.Google ScholarGoogle Scholar
  15. Z. Hai, P. Zhao, P. Cheng, P. Yang, X. Li, and G. Li, "Deceptive Review Spam Detection via Exploiting Task Relatedness and Unlabeled Data," in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 1817--1826, 2016.Google ScholarGoogle Scholar
  16. A. Heydari, M. Tavakoli, and N. Salim, "Detection of fake opinions using time series," Expert Systems with Applications., vol. 58, pp. 83--92, 2016. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. R. G. Adike, and V. Reddy, "Detection of Fake Review and Brand Spam Using Data Mining Technique," in International Journal of Recent Trends in Engineering & Research, pp. 252--256, 2016.Google ScholarGoogle Scholar
  18. X. Wang, K. Liu, S. He, and J. Zhao, "Learning to Represent Review with Tensor Decomposition for Spam Detection," in Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, pp. 866--875, 2016.Google ScholarGoogle Scholar
  19. M. Crawford, T. M. Khoshgoftarr, and J. D. Prusa, "Reducing Feature Set Explosion to Facilitate Real-World Review Spam Detection," in Proceedings of the Twenty-Ninth International Florida Artificial Intelligence Research Society Conference, pp. 304--309, 2016.Google ScholarGoogle Scholar
  20. S. Bajaj, N. Garg, and S. K. Singh, "A Novel User-based Spam Review Detection," Information Technology and Quantitative Management., vol. 122, pp. 1009--1015, 2017.Google ScholarGoogle Scholar
  21. H. Li, G. Fei, S. Wang, A. Mukherjee, and J. Shao, "Bimodal Distribution and Co-Bursting in Review Spam Detection," in International World Wide Web Conference Committee, pp. 1063--1072, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. J. K. Rout, S. Sing, S. K. Jena, and S. Bakshi, "Deceptive review detection using labeled and unlabeled data," Multimedia Tools and Applications., vol. 76, no. 3, pp. 3187--3211, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. L. Li, B. Qin, W. Ren, and T. Liu, "Document representation and feature combination for deceptive spam review detection," Neurocomputing., vol. 254, pp. 33--41, 2017.Google ScholarGoogle ScholarCross RefCross Ref
  24. X. Wang, K. Liu, and J. Zhao, "Handling Cold-Start Problem in Review Spam Detection by Jointly Embedding Texts and Behaviors," in Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pp. 366--376, 2017.Google ScholarGoogle Scholar
  25. S. P. Rajamohana, and K. Umamaheswari, "Hybrid Optimization Algorithm of Improved Binary Particle Swarn Optimization (iBPSO) and Cuckoo Search for Review Spam Detection," in Proceedings of the 9th International Conference on Machine Learning and Computing, pp. 238--242, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Y. Ren, and D. Ji, "Neural networks for deceptive opinion spam detection: An empirical study," Information Sciences., vol. 385--386, pp. 213--224, 2017. Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. W. Etaiwi, and G. Naymat, "The Impact of applying Different Preprocessing Steps on Review Spam Detection," in The 8th International Conference on Emerging Ubiquitous Systems and Pervasive Networks, pp. 273--279, 2017.Google ScholarGoogle Scholar
  28. Z. You, T. Qian, and B. Liu, "An Attribute Enhanced Domain Adaptive Model for Cold-Start Spam Review Detection," in Proceedings of the 27th International Conference on Computational Linguistics, pp. 1884--1895, 2018.Google ScholarGoogle Scholar
  29. S. Saunya, and J. P. Singh, "Detection of spam reviews: a sentiment analysis approach," in Computer Science and Information Technology, pp. 137--148, 2018.Google ScholarGoogle Scholar
  30. Z. Wang, S. Gu, X. Zhao, and X. Xu, "Graph-based review spammer group detection," Knowledge Information System., vol. 55, pp. 571--597, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. M. H. Arif, J. Li, M. Iqbal, and K. Liu, "Sentiment analysis and spam detection in short informal text using learning classifier systems," Soft Computing., vol. 22, pp. 7281--7291, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. F. Khurshid, Y. Zhu, Z. Xu, M. Ahmad, and M. Ahmad, "Enactment of Ensemble Learning for Review Spam Detection on Selected Features," International Journal of Computational Intelligence Systems., vol. 12, no. 1, pp. 387--394, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  33. C. Yuan, W. Zhou, Q. Ma, S. Lv, J. Han, and S. Hu, "Learning review representations form user and product level information for spam detection," in International Conference on Data Mining, 2019.Google ScholarGoogle Scholar
  34. A. C. Pandey, and D. S. Rajpoot, "Spam review detection using spiral cuckoo search clustering method," Evolutionary Intelligence., vol. 12, pp. 147--164, 2019.Google ScholarGoogle ScholarCross RefCross Ref
  35. A. Li, Z. Qin, R. Liu, Y. Yang, and D. Li, "Spam Review Detection with Graph Convolutional Networks," in Proceedings of the 28th ACM International Conference on Information and Knowledge, pp. 2709--2711. 2019. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. A. Mukherjee, B. Liu, and N. Glance, "Spotting fake reviewer groups in consumer reviews," in Proceedings of the 21st international conference on World Wide Web. ACM, pp. 191--200, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. L. Wu, X. Hu, F. Morstatter, and H. Liu, "Adaptive Spammer Detection with Sparse Group Modelling," in Proceedings of the Eleventh International AAAI Conference on Web and Social Media, pp. 319--326, 2017.Google ScholarGoogle Scholar
  38. H. Chen, J. Liu, Y. Lv, M. H. Li, M. Liu, and Q. Zheng, "Semi-supervised clue fusion for spammer detection in Sina Weibo," Information Fusion., vol. 44, pp. 22--32, 2018.Google ScholarGoogle ScholarCross RefCross Ref
  39. S. Rathore, V. Loia, and J. H. Park, "SpamSpotter: An efficient spammer detection framework based on intelligent decision support system on Facebook," Applied Soft Computing., vol. 67, pp. 920--932, 2018. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. Y. Zhang, H. Zhang, X. Yuan, and N. Tzeng, "TweetScore: Scoring Tweets via Social Attribute Relationships for Twitter Spammer Detection," in The 14th ACM ASIA Conference on Computer and Communications Security, pp. 379--390, 2019. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. W. Pei, Y. Xie, and G. Tang, "Spammer Detection via Combined Neural Network," in International Conference on Machine Learning and Data Mining in Pattern Recognition, pp. 350--364, 2018.Google ScholarGoogle Scholar

Index Terms

  1. Detection of online review spam: a literature review

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      SIET '20: Proceedings of the 5th International Conference on Sustainable Information Engineering and Technology
      November 2020
      277 pages
      ISBN:9781450376051
      DOI:10.1145/3427423

      Copyright © 2020 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 28 December 2020

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      SIET '20 Paper Acceptance Rate45of57submissions,79%Overall Acceptance Rate45of57submissions,79%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader