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Fake reviews detection: A survey


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Mohawesh, R ORCID: 0000-0002-9332-3487, Xu, S ORCID: 0000-0003-0597-7040, Tran, SN ORCID: 0000-0002-5912-293X, Ollington, R ORCID: 0000-0001-7533-2307, Springer, M ORCID: 0000-0003-3017-2893, Jararweh, Y and Maqsood, S 2021 , 'Fake reviews detection: A survey' , IEEE Access, vol. 9 , pp. 65771-65802 , doi: 10.1109/ACCESS.2021.3075573.

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In e-commerce, user reviews can play a significant role in determining the revenue of an organisation. Online users rely on reviews before making decisions about any product and service. As such, the credibility of online reviews is crucial for businesses and can directly affect companies’ reputation and profitability. That is why some businesses are paying spammers to post fake reviews. These fake reviews exploit consumer purchasing decisions. Consequently, the techniques for detecting fake reviews have extensively been explored in the past twelve years. However, there still lacks a survey that can analyse and summarise the existing approaches. To bridge up the issue, this survey paper details the task of fake review detection, summing up the existing datasets and their collection methods. It analyses the existing feature extraction techniques. It also summarises and analyses the existing techniques critically to identify gaps based on two groups: traditional statistical machine learning and deep learning methods. Further, we conduct a benchmark study to investigate the performance of different neural network models and transformers that have not been used for fake review detection yet. The experimental results on two benchmark datasets show that RoBERTa performs about 7% better than the state-of-the-art methods in a mixed domain for the deception dataset with the highest accuracy of 91.2%, which can be used as a baseline for future studies. Finally, we highlight the current gaps in this research area and the possible future directions.

Item Type: Article
Authors/Creators:Mohawesh, R and Xu, S and Tran, SN and Ollington, R and Springer, M and Jararweh, Y and Maqsood, S
Keywords: fake review, fake review detection, feature engineering, machine learning, deep learning
Journal or Publication Title: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers
ISSN: 2169-3536
DOI / ID Number: 10.1109/ACCESS.2021.3075573
Copyright Information:

© 2021. The Authors. This is an open access article under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) License, (, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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