Abstract
Online spam comments often misguide users during online shopping. Existing online spam detection methods rely on semantic clues, behavioral footprints, and relational connections between users in review systems. Although these methods can successfully identify spam activities, evolving fraud strategies can successfully escape from the detection rules by purchasing positive comments from massive random users, i.e., user Cloud. In this paper, we study a new problem, Collective Marketing Hyping detection, for spam comments detection generated from the user Cloud. It is defined as detecting a group of marketing hyping products with untrustful marketing promotion behaviour. We propose a new learning model that uses heterogenous product networks extracted from product review systems. Our model aims to mining a group of hyping activities, which differs from existing models that only detect a single product with hyping activities. We show the existence of the Collective Marketing Hyping behavior in real-life networks. Experimental results demonstrate that the product information network can effectively detect fraud intentional product promotions.
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Notes
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On the holiday, the most popular E-commercial platform in China will appeal the store owner to give special discount or organize group-shopping and flash sale activities, which will significantly increase the store’s sale or reviews.
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Acknowledgements
This work was supported by Australia ARC Discovery Project (DP140102206) and Australia Linkage Project (LP150100671).
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© 2016 Springer International Publishing Switzerland
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Zhang, Q., Zhang, Q., Long, G., Zhang, P., Zhang, C. (2016). Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping Behavior. In: Bailey, J., Khan, L., Washio, T., Dobbie, G., Huang, J., Wang, R. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2016. Lecture Notes in Computer Science(), vol 9651. Springer, Cham. https://doi.org/10.1007/978-3-319-31753-3_4
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DOI: https://doi.org/10.1007/978-3-319-31753-3_4
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