eCite Digital Repository

Fake reviews detection: A survey


Mohawesh, R and Xu, S and Tran, SN and Ollington, R and Springer, M and Jararweh, Y and Maqsood, S, Fake reviews detection: A survey, IEEE Access, 9 pp. 65771-65802. ISSN 2169-3536 (2021) [Refereed Article]

PDF (Published version)

Copyright Statement

© 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.

DOI: doi:10.1109/ACCESS.2021.3075573


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 companiesí 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 Details

Item Type:Refereed Article
Keywords:fake review, fake review detection, feature engineering, machine learning, deep learning
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Artificial intelligence not elsewhere classified
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Mohawesh, R (Mr Rami Mohawesh)
UTAS Author:Xu, S (Dr Shuxiang Xu)
UTAS Author:Tran, SN (Dr Son Tran)
UTAS Author:Ollington, R (Dr Robert Ollington)
UTAS Author:Springer, M (Dr Matthew Springer)
UTAS Author:Maqsood, S (Ms Sumbal Maqsood)
ID Code:144276
Year Published:2021
Web of Science® Times Cited:11
Deposited By:Information and Communication Technology
Deposited On:2021-05-06
Last Modified:2021-09-09
Downloads:35 View Download Statistics

Repository Staff Only: item control page