Fake Review Detection System: A Review

Abstract

Fake reviews, often referred to as deceptive or dishonest reviews, have become a significant concern for both businesses and consumers (Feng et al., 2016). These reviews are deliberately crafted to mislead or manipulate the opinions of others and can be driven by various motives, such as financial gain, competition, or personal grudges (Liu et al., 2018). The impact of fake reviews on the reputation and sales of products or services can be substantial, leading consumers to make misguided purchasing decisions (Wang et al., 2017). The demand for effective methods to detect and address fake reviews has been steadily increasing, aiming to assist businesses and consumers in identifying and mitigating the influence of such reviews (Hu et al., 2019). However, detecting fake reviews presents challenges due to the diverse forms they can take and the difficulty in distinguishing them from genuine reviews (Xu et al., 2020). To tackle this challenge, researchers have developed various approaches and techniques, including machine learning, natural language processing, crowd-sourced annotation, and content-based methods (Feng et al., 2016; Liu et al., 2018). This review provides a comprehensive overview of the current state of the art in fake review detection methods, highlighting the key challenges and limitations associated with these approaches. The implications of these methods for businesses and consumers are also discussed, and future research directions are suggested to further enhance the effectiveness and reliability of fake review detection techniques.