Fake Review Detection Using Machine learning and Deep Learning

Abstract

The widespread adoption of Web 2.0 platforms has enabled consumers to share their opinions on products and services, influencing purchasing decisions. However, the proliferation of spam reviews has undermined the credibility of online reviews. This study aims to identify and evaluate existing approaches for detecting reviews, individual spammers, and Organizations. We categorized machine learning (ML) and deep learning (DL) techniques used for Review detection and assessed their effectiveness. Our findings indicate that accuracy is the most frequently used metric (25%), followed by recall (24%) and precision (22%). Additionally, we observed that utilizing the entire Amazon dataset can enhance the performance of F-measure, AUC, and F1-score metrics by 7%. Our study concludes that SMS spam filtering strategies are often effective in combating spam reviews. Furthermore, we developed a taxonomy of existing methodologies and observed a significant number of studies employing SMS anti-spam applications. This research uncovered innovative applications of ML and DL to spam review detection, offering a novel approach to addressing this issue. Our findings provide both academics and practitioners with a deeper understanding of the challenges in spam review identification and potential avenues for improvement using ML techniques.