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Fake Restaurant Review Detection Using Deep Neural Networks with Hybrid Feature Fusion Method

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Database Systems for Advanced Applications (DASFAA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13247))

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Abstract

Online review websites all over the world have developed by leaps and bounds in the past decade, followed by more and more fake reviews that are very difficult to identify. This not only affects the decision-making of consumers, but also seriously undermines the fairness of the consumption market. Most existing research ignored the abundant metadata in reviews and its effective combination with the review text. In this paper, we firstly collect reviews from one of the most popular Chinese review platforms Dianping, and release two fake review datasets we construct, which makes up for the relative lack of datasets in this field. Afterward, we present some novel and effective features for fake review detection by analyzing the characteristics of the collected reviews. In addition, we propose a multimodal fake review detection model BAM (BERT + Attention + MLP), which takes the review text and the extracted features as input, and uses neural networks as well as multimodal fusion technology to realize the recognition of fake reviews. The experimental results show that BAM has low dependence on the size of the dataset. Compared with baseline models, it can be found that BAM has a better detection performance.

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Notes

  1. 1.

    https://www.dianping.com.

  2. 2.

    https://www.lboro.ac.uk/gawc/world2020t.html.

  3. 3.

    https://github.com/FakeReview/Dataset.

  4. 4.

    https://github.com/isnowfy/snownlp.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (NSFC) under grant nos. 61802270, 61802271, 81602935, and 81773548. In addition, this work is also partially supported by Joint Research Fund of China Ministry of Education and China Mobile Company (No. CM20200409), the Key Research and Development Program of Science and Technology Department of Sichuan Province (No. 2020YFS0575), and the Sichuan University and Yibin Municipal People’s Government University and City Strategic Cooperation Special Fund Project (Grant No. 2020CDYB-29).

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Sichuan University, Chengdu, China

    Yifei Jian, Xingshu Chen & Haizhou Wang

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  1. Yifei Jian
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  2. Xingshu Chen
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  3. Haizhou Wang
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Corresponding author

Correspondence to Haizhou Wang .

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Editors and Affiliations

  1. Indian Institute of Technology Kanpur, Kanpur, India

    Arnab Bhattacharya

  2. National University of Singapore, Singapore, Singapore

    Janice Lee Mong Li

  3. University of California, Santa Barbara, Santa Barbara, CA, USA

    Divyakant Agrawal

  4. IIIT Hyderabad, Hyderabad, India

    P. Krishna Reddy

  5. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Mukesh Mohania

  6. Ashoka University, Sonepat, Haryana, India

    Anirban Mondal

  7. Indraprastha Institute of Information Technology Delhi, New Delhi, India

    Vikram Goyal

  8. University of Aizu, Aizu, Japan

    Rage Uday Kiran

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Jian, Y., Chen, X., Wang, H. (2022). Fake Restaurant Review Detection Using Deep Neural Networks with Hybrid Feature Fusion Method. In: Bhattacharya, A., et al. Database Systems for Advanced Applications. DASFAA 2022. Lecture Notes in Computer Science, vol 13247. Springer, Cham. https://doi.org/10.1007/978-3-031-00129-1_9

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  • DOI: https://doi.org/10.1007/978-3-031-00129-1_9

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  • Online ISBN: 978-3-031-00129-1

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