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Deep feature fusion for cold-start spam review detection

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Abstract

The cold-start problem in spam review detection is a significant challenge referring to identifying the authenticity of the first review posted by new users. For generating more sensitive features to identify new reviews, existing methods mainly leverage text-similarity of review to find relevant features to approximate the incomplete behavior features of new reviews. However, they over-rely on the text information of new reviews while ignoring the mutual behavioral information in the review system, leading to a decrease in the sensitivity of features. To address the issue, we propose a deep feature fusion method, which balances the importance of text information and behavior information to enhance features’ sensitivity. Specifically, we construct a heterogeneous graph, where products and users serve as vertices connected by edges representing reviews. Then, we perform graph convolution calculation on this graph in the first feature fusion stage. We utilize the mutual behavioral information in the review system to compensate for the incomplete behavior feature of new reviews. Furthermore, we design a co-attention network, which can give features different weights in the global feature fusion stage, to gain features with high sensitivity of identifying new reviews. Extensive experiments on Yelp-hotel and Yelp-restaurant datasets demonstrate that our proposed approach yields better classification performance over existing methods.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to privacy and confidentiality agreements as well as other restrictions, but are available from the corresponding author on reasonable request.

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Acknowledgements

This project is supported by National Natural Science Foundation of China under Grant 61972057, and 62172059, Hunan Provincial Natural Science Foundation of China under Grant 2022JJ30623, Scientific Research Fund of Hunan Provincial Education Department of China under Grant 21A0211, Hunan Provincial Innovation Foundation For Postgraduate under Grant CX20210812.

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Authors and Affiliations

  1. Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang, Huiqing You & Guoqing Guo

  3. Hunan Provincial Key Laboratory of Smart Roadway and Cooperative Vehicle-Infrastructure Systems, Changsha University of Science and Technology, Changsha, 410114, China

    Lingyun Xiang

  4. North China Institute of Computing Technology, Beijing, 100083, China

    Qian Li

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  1. Lingyun Xiang
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  2. Huiqing You
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Correspondence to Huiqing You.

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Xiang, L., You, H., Guo, G. et al. Deep feature fusion for cold-start spam review detection. J Supercomput 79, 419–434 (2023). https://doi.org/10.1007/s11227-022-04685-z

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