Spam Detection Using Rating and Review Processing Method

  • Ridhima GhaiEmail author
  • Sakshum KumarEmail author
  • Avinash Chandra PandeyEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 670)


In recent times, e-commerce sites have become an essential part of people lifestyle. Viewers give feedback and firsthand account of the online products, and these reviews thus play an important role in decision making of the other buyers. So, in order to increase or decrease sales of products, spam reviews are generated by the companies. Hence, there is a need to detect and filter the spam reviews to provide customers genuine reviews of the product. In this paper, a review processing method is proposed. Some parameters have been suggested to find the usefulness of reviews. These parameters show the variation of a particular review from other, thus increasing the probability of it being spam. This method introduced classifies the review as helpful or non-helpful depending on the score assigned to the review.


Rating deviation Caps count Reviewer’s count Data scrapping 


  1. 1.
    Y. Lin, T. Zhu, X. Wang, J. Zhang, and A. Zhou, “Towards online review spam detection,” in Proceedings of the 23rd International Conference on World Wide Web. ACM, 2014, pp. 341–342.Google Scholar
  2. 2.
    A. Heydari, M. ali Tavakoli, N. Salim, and Z. Heydari, “Detection of review spam: A survey,” Expert Systems with Applications, vol. 42, no. 7, pp. 3634–3642, 2015.CrossRefGoogle Scholar
  3. 3.
    S. Xie, G. Wang, S. Lin, and P. S. Yu, “Review spam detection via temporal pattern discovery,” in Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2012, pp. 823–831.Google Scholar
  4. 4.
    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 Advances in Social Networks Analysis and Mining (ASONAM), 2014 IEEE/ACM International Conference on. IEEE, 2014, pp. 261–264.Google Scholar
  5. 5.
    E.-P. Lim, V.-A. Nguyen, N. Jindal, B. Liu, and H. W. Lauw, “Detecting product review spammers using rating behaviors,” in Proceedings of the 19th ACM international conference on Information and knowledge management. ACM, 2010, pp. 939–948.Google Scholar
  6. 6.
    G. Wang, S. Xie, B. Liu, and P. S. Yu, “Identify online store review spammers via social review graph,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 3, no. 4, p. 61, 2012.Google Scholar
  7. 7.
    M. Jiang, P. Cui, and C. Faloutsos, “Suspicious behavior detection: Current trends and future directions,” IEEE Intelligent Systems, vol. 31, pp. 31–39, 2016.CrossRefGoogle Scholar
  8. 8.
    M. Sasaki and H. Shinnou, “Spam detection using text clustering,” in Cyberworlds, 2005. International Conference on. IEEE, 2005.Google Scholar
  9. 9.
    R. Patel and P. Thakkar, “Opinion spam detection using feature selection,” in Computational Intelligence and Communication Networks (CICN), 2014 International Conference on. IEEE, 2014, pp. 560–564.Google Scholar
  10. 10.
    X. Li and X. Yan, “A novel chinese text mining method for e-commerce review spam detection,” in International Conference on Web-Age Information Management. Springer, 2016, pp. 95–106.Google Scholar
  11. 11.
    L. Wu, X. Hu, F. Morstatter, and H. Liu, “Adaptive spammer detection with sparse group modeling,” 2017.Google Scholar
  12. 12.
    J. G. Thanikkal and M. Danish, “A novel approach to improve spam detection using sds algorithm,” International Journal for Innovative Research in Science and Technology, vol. 1, no. 12, pp. 306–310, 2015.Google Scholar
  13. 13.
    S. Rayana and L. Akoglu, “Collective opinion spam detection: Bridging review networks and metadata,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2015, pp. 985–994.Google Scholar
  14. 14.
    Y. Shao, M. Trovati, Q. Shi, O. Angelopoulou, E. Asimakopoulou, and N. Bessis, “A hybrid spam detection method based on unstructured datasets,” Soft Computing, vol. 21, pp. 233–243, 2017.CrossRefGoogle Scholar
  15. 15.
    J. K. Rout, S. Singh, S. K. Jena, and S. Bakshi, “Deceptive review detection using labeled and unlabeled data,” Multimedia Tools and Applications, pp. 1–25, 2016.CrossRefGoogle Scholar
  16. 16.
    D. H. Fusilier, M. Montes-y Gómez, P. Rosso, and R. G. Cabrera, “Detection of opinion spam with character n-grams,” in International Conference on Intelligent Text Processing and Computational Linguistics. Springer, 2015, pp. 285–294.Google Scholar
  17. 17.
    M. I. Ahsan, T. Nahian, A. A. Kafi, M. I. Hossain, and F. M. Shah, “Review spam detection using active learning,” in Information Technology, Electronics and Mobile Communication Conference (IEMCON), 2016 IEEE 7th Annual. IEEE, 2016, pp. 1–7.Google Scholar
  18. 18.
    H. Xue, F. Li, H. Seo, and R. Pluretti, “Trust-aware review spam detection,” in Trustcom/BigDataSE/ISPA, 2015 IEEE, vol. 1. IEEE, 2015, pp. 726–733.Google Scholar
  19. 19.
    G. Ansari, T. Ahmad, and M. Doja, “Review ranking method for spam recognition,” in Contemporary Computing (IC3), 2016 Ninth International Conference on. IEEE, 2016, pp. 1–5.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Jaypee Institute of Information TechnologyNoidaIndia

Personalised recommendations