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Explainable knowledge integrated sequence model for detecting fake online reviews

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Abstract

Online reviews have a great influence on customers’ shopping decisions. However, countless fake reviews are posted on shopping platforms, which seriously interfere with customers’ shopping decisions and pollute the fair e-commerce environment. In this paper, we propose EKI-SM, an explainable knowledge integrated sequence model, to detect fake reviews. Compared with existing models, the EKI-SM displays four advantages: 1) It integrates a set of important knowledge and learns high-dimensional word embedding from reviews to guide fake review detection tasks; in addition, this knowledge explains the results of the model. 2) It learns a continuous sequence model from discrete observations with high-dimensional features, which helps to learn more discriminating fake review features. 3) It fuses the one-dimensional convolutional network, the long short-term memory network, and the residual connector to capture the local and global dependency of the sequence and make the prediction model more robust. 4) Inspired by the idea of interpretable deep learning, we explain the EKI-SM and find the important critical words for detecting fake online reviews, which derive some interesting insights. Experiments on actual fake review datasets demonstrate that the EKI-SM achieves higher accuracy in fake review detection than that of other state-of-the-art methods; indeed, it benefits from the integration of knowledge and multi-modal features.

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Code Availability

The code uses python language programming to run on PyCharm, the code is available.

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Acknowledgments

This work is supported by the National Nature Science Foundation of China (No.61672329, No.62072290,No. 81871508, No. 61773246); Major Program of Shandong Province Natural Science Foundation (ZR2019ZD04, No. ZR2018ZB0419); Shandong Provincial Project of Education Scientific Plan (No.SDYY18058).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, 250358, China

    Shu Han, Hong Wang, Wei Li, Hui Zhang & Luhe Zhuang

Authors
  1. Shu Han
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  2. Hong Wang
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  3. Wei Li
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  4. Hui Zhang
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  5. Luhe Zhuang
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Han Shu and Wang Hong. The first draft of the manuscript was written by Han Shu and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Hong Wang.

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The authors declared that they have no conflicts of interest to this work.

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The data set can be obtained at the link below http://myleott.com/op-spam.html

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Han, S., Wang, H., Li, W. et al. Explainable knowledge integrated sequence model for detecting fake online reviews. Appl Intell 53, 6953–6965 (2023). https://doi.org/10.1007/s10489-022-03822-8

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