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Exploring Heterogeneous Product Networks for Discovering Collective Marketing Hyping Behavior

  • Qinzhe ZhangEmail author
  • Qin Zhang
  • Guodong Long
  • Peng Zhang
  • Chengqi Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9651)

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.

Keywords

Regularization Term Product Network User Cloud Online Store Spam Detection 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work was supported by Australia ARC Discovery Project (DP140102206) and Australia Linkage Project (LP150100671).

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Qinzhe Zhang
    • 1
    Email author
  • Qin Zhang
    • 1
  • Guodong Long
    • 1
  • Peng Zhang
    • 1
  • Chengqi Zhang
    • 1
  1. 1.The University of Technology SydneySydneyAustralia

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