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Fake Comment Detection Based on Time Series and Density Peaks Clustering

  • Ruitong Di
  • Hong Wang
  • Youli Fang
  • Ying Zhou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11338)

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%.

Keywords

Fake review Time series Multi-scale Noise Density peaks clustering 

Notes

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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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Ruitong Di
    • 1
    • 2
  • Hong Wang
    • 1
    • 2
    • 3
  • Youli Fang
    • 1
    • 2
  • Ying Zhou
    • 1
    • 2
  1. 1.School of Information Science and EngineeringShandong Normal UniversityJinanChina
  2. 2.Shandong Provincial Key Laboratory for Distributed Computer Software Novel TechnologyJinanChina
  3. 3.Institute of Biomedical SciencesShandong Normal UniversityJinanChina

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