Abstract
Spammers generate fake reviews to influence the reputation of products. By grouping together, spammers can dramatically alter how products are perceived. Different from previous research, which has mostly used behavioral indicators and structural indicators, we propose a new perspective on spammer detection. In our approach, we portray reviewers as a comment-based reviewer network through a new collusion similarity measure, divide reviewers into different communities using an effective community detection method and separate spammer communities from normal reviewer communities through network structure. We find that spammer communities have different network structural features from normal reviewer communities, a high clustering coefficient and high self-similarity. In our experiments, we show that our method achieves a detection accuracy of 94.59% - substantially higher than the current state-of-the-art methods which achieve an 80.00% accuracy.
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Acknowledgments
This research is supported by the National Natural Science Foundation of China (Grant No. 71203135). Thank Nikita Koptyug for proofreading our manuscript.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Zhou, W., Liu, M., Zhang, Y. (2018). Detecting Spammer Communities Using Network Structural Features. In: Romdhani, I., Shu, L., Takahiro, H., Zhou, Z., Gordon, T., Zeng, D. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 252. Springer, Cham. https://doi.org/10.1007/978-3-030-00916-8_61
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DOI: https://doi.org/10.1007/978-3-030-00916-8_61
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