Abstract
This paper proposes a fake comment recognition method based on time series and density peaks clustering. Firstly, an anomaly recognition model based on multi-dimensional time series is constructed. Secondly, according to the idea of multi-scale features, seven benchmark-scale and corresponding subdivision-scale features are extracted hierarchically, and further, 49 features are finally obtained. At last, an optimized detection model based on density peaks clustering is proposed for identifying the fake comments, so as to improve the anti-noise ability of our method. The effectiveness of our proposed method is verified by several experiments, with the AUC value reaching 92%.
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References
Jindal, N., Liu, B.: Opinion spam and analysis. In: International Conference on Web Search and Data Mining, pp. 219–230. ACM (2008)
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, J., Ott, M., Cardie, C., et al.: Towards a general rule for identifying deceptive opinion spam. In: Meeting of the Association for Computational Linguistics, pp. 1566–1576 (2014)
Li, H., Liu, B., Mukherjee, A., et al.: Spotting fake reviews using positive-unlabeled learning. Computacion Y Sistemas 18(3), 467–475 (2015)
Song, H.X., Yan, X., Yu, Z.T., et al.: Detection of fake reviews based on adaptive clustering. J. Nanjing Univ. 49(4), 433–438 (2013)
Gao, J., Dong, Y.W., Shang, M., et al.: Group-based ranking method for online rating systems with spamming attacks. EPL 110(2), 1–6 (2015)
Husna, H., Phithakkitnukoon, S., Palla, S., et al.: Behavior analysis of spam botnets (2007)
Craigmile, P.F.: All of statistics: a concise course in statistical inference. J. Roy. Stat. Soc. 168(1), 203–204 (2005)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492 (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)
Shao, Z., Ji, D.: Spotting fake reviewers based on sentiment features and users’ relationship. Comput. Appl. Softw. 33(5), 158–161 (2016)
Acknowledgments
This work is supported by the National Nature Science Foundation of China (No. 61672329, No. 61373149, No. 61472233, No. 61572300, No. 81273704), Shandong Provincial Project of Education Scientific Plan (No. ZK1437B010).
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Di, R., Wang, H., Fang, Y., Zhou, Y. (2018). Fake Comment Detection Based on Time Series and Density Peaks Clustering. 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_15
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DOI: https://doi.org/10.1007/978-3-030-05234-8_15
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