The Library
Fraudulent review detection model focusing on emotional expressions and explicit aspects : investigating the potential of feature engineering
Tools
Kumar, Ajay, Gopal, Ram D., Shankar, Ravi and Tan, Kim Hua (2022) Fraudulent review detection model focusing on emotional expressions and explicit aspects : investigating the potential of feature engineering. Decision Support Systems, 155 . 113728. doi:10.1016/j.dss.2021.113728 ISSN 0167-9236.
|
PDF
WRAP-Fraudulent-review-detection-model-focusing-emotional-expressions-engineering-2021.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (1886Kb) | Preview |
Official URL: http://dx.doi.org/10.1016/j.dss.2021.113728
Abstract
Reading customer reviews before purchasing items online has become a common practice; however, some companies use machine learning (ML) algorithms to generate false reviews in order to create positive brand images of their own products and negative images of competitors' offerings. Existing techniques use review content to identify fraudulent reviewers; however, spammers become more intelligent, started to learn from their mistakes, and changed their tactics in order to avoid detection techniques. Thus, investigating fraudulent accounts' behaviour of generating fake negative or positive reviews for competitors or themselves and the necessity of ML classifiers to identify fraudulent reviews, is more important than ever. In this research, we present a novel feature engineering approach in which we (1) extract several “review-centric” and “reviewer-centric” features from a dataset; (2) combine the cumulative effects of features distributions into a unified model that represents overall behavior of the fraudulent reviewers; (3) investigate the role of effective data pre-processing to improve detection accuracy; and (4) develop a probabilistic approach to detect fraudulent reviewers by learning a novel M-SMOTE model over a derived balanced dataset and feature distributions, which outperforms other ML models. Our study contributes to the literature on digital platforms and fraudulent review detection with significant managerial and theoretical implications through these novel findings.
Item Type: | Journal Article | ||||||||
---|---|---|---|---|---|---|---|---|---|
Subjects: | H Social Sciences > HF Commerce Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||||||
Divisions: | Faculty of Social Sciences > Warwick Business School | ||||||||
Library of Congress Subject Headings (LCSH): | Electronic data processing -- Distributed processing, Service-oriented architecture (Computer science), Consumer behavior, Machine learning | ||||||||
Journal or Publication Title: | Decision Support Systems | ||||||||
Publisher: | Elsevier BV | ||||||||
ISSN: | 0167-9236 | ||||||||
Official Date: | April 2022 | ||||||||
Dates: |
|
||||||||
Volume: | 155 | ||||||||
Article Number: | 113728 | ||||||||
DOI: | 10.1016/j.dss.2021.113728 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Restricted or Subscription Access | ||||||||
Date of first compliant deposit: | 13 January 2022 | ||||||||
Date of first compliant Open Access: | 5 July 2023 |
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |
Downloads
Downloads per month over past year