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Node embedding approach for accurate detection of fake reviews: a graph-based machine learning approach with explainable AI

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Abstract

In recent years, online reviews have become increasingly important in promoting various products and services. Unfortunately, writing deceptive reviews has also become a common practice to promote one’s own business or tarnish the reputation of competitors. As a result, identifying fake reviews has become an intense and ongoing area of research. This paper proposes a node embedding approach to detect online fake reviews. The approach involves extracting features from the input data to create a distance matrix, which is then used to construct a Graph. From the graph, we extract graph nodes and use the Node2Vec biased random walk algorithm to create a model. We retrieve node embeddings from the Node2Vec model using Word2Vec and use different classifiers to classify the nodes. We trained and evaluated the machine learning models on the Deceptive Opinion Spam Corpus and YelpChi datasets and achieved superior results compared to prior work for detecting fake reviews, with SVM using the Hamming distance achieving 98.44% accuracy, 98.44% precision, 98.44% recall, and 98.44% F1-score. Furthermore, we explored different methods for explaining our proposed methods using explainable AI, demonstrating the interpretability of our approach. Our proposed node embedding approach shows promising results for detecting fake reviews and offers a transparent and interpretable solution for the problem.

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Data availability

The data and code used in the study are available from https://github.com/nzaki02/Fake_Review_2023.

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Acknowledgements

The authors gratefully acknowledge the partial support received from the College of Information Technology (CIT) at the United Arab Emirates University (UAEU). In addition, the authors would like to thank the Research Office at the UAEU for providing a summer grant (Grant code: G00003895) that supported the research work presented in this paper.

Funding

The work is supported by the Research Office at the UAEU (Grant code: G00003895)

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, College of Information Technology, United Arab Emirates University, Al Ain, 15551, UAE

    Nazar Zaki, Anusuya Krishnan, Sherzod Turaev, Zahiriddin Rustamov, Jaloliddin Rustamov, Aisha Almusalami, Tsion Regasa & Brice Boris Iriho

  2. Department of Information Systems and Security, College of Information Technology, United Arab Emirates University, Al Ain, 15551, UAE

    Farah Ayyad

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  1. Nazar Zaki
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  2. Anusuya Krishnan
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Contributions

NZ and ST conceptualized the paper. All authors contributed to the experimental work, with NZ, AK, ST, ZR, and JR contributing to the writing of the manuscript. NZ provided project supervision.

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Correspondence to Nazar Zaki.

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Zaki, N., Krishnan, A., Turaev, S. et al. Node embedding approach for accurate detection of fake reviews: a graph-based machine learning approach with explainable AI. Int J Data Sci Anal (2024). https://doi.org/10.1007/s41060-024-00565-2

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