Abstract
Online reviews can help people get more information about stores and products. The potential customers tend to make decisions according to them. However, driven by profit, spammers post fake reviews to mislead the customers by promoting or demoting target store. Previous studies mainly utilize the rating as an indicator for detection. However, these studies ignore an important problem that the rating cannot represent the sentiment accurately. In this paper, we propose a method of identifying fake reviews based on rating- review consistency and multi-dimensional time series. We first incorporate the sentiment analysis techniques into fake review detection. Then, we further discuss the relationship between ratings and fake reviews. In the end, this paper establishes an effective time series to detect fake reviews. Experimental results show that our proposed methods have good detection result and outperform state-of-art methods.
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References
Ntoulas, A., Najork, M., Manasse, M.: Detecting spam web pages through content analysis. In: Proceedings of the 15th International Conference on World Wide Web, Edinburgh, Scotland, pp. 83–92 (2006)
Yan, L., Zhu, T., Wu, H.: Towards online anti-opinion spam: spotting fake reviews from the review sequence. In: Proceedings of IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, Beijing, China, pp. 261–264 (2014)
Ren, Y., Yin, L., Ji, D.: Deceptive reviews detection based on Language structure and sentiment polarity. J. Front. Comput. Sci. Technol. 8(3), 313–320 (2014)
Chang, T., Hsu, P.Y., Cheng, M.S., Chung, C.Y., Chung, Y.L.: Detecting fake review with rumor model—case study in hotel review. In: He, X., et al. (eds.) IScIDE 2015. LNCS, vol. 9243, pp. 181–192. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23862-3_18
Li, H., Chen, Z., Liu, B.: Spotting fake reviews via collective positive-unlabeled learning. In: Proceedings of IEEE International Conference on Data Mining, Washington, USA, pp. 899–904 (2014)
Dewang, R.K., Singh, A.K.: Identification of fake reviews using new set of lexical and syntactic features. In: Proceedings of the Sixth International Conference on Computer and Communication Technology, Allahabad, India, 115–119 (2015)
Xie, S., Wang, G., Lin, S.: Review spam detection via temporal pattern discovery. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Beijing, China, pp. 823–831 (2012)
Shao, Z., Ji, D.: Spotting fake reviewers based on sentiment features and users’ relationship. Comput. Appl. Softw. 33(5), 158–161 (2016)
Feng, S., Banerjee, R., Choi, Y.: Syntactic stylometry for deception detection. In: Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, Jeju Island, Korea, pp. 171–175 (2012)
Acknowledgments
This work is supported by Guangzhou scholars project for universities of Guangzhou (No. 1201561613).
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Youli, F., Hong, W., Ruitong, D., Lutong, W., Li, J. (2018). Detecting Fake Reviews Based on Review-Rating Consistency and Multi-dimensional Time Series. In: Hu, T., Wang, F., Li, H., Wang, Q. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2018. Lecture Notes in Computer Science(), vol 11338. Springer, Cham. https://doi.org/10.1007/978-3-030-05234-8_14
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DOI: https://doi.org/10.1007/978-3-030-05234-8_14
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