Advancements and Challenges in Automated Fake Review Detection

Abstract

The proliferation of online platforms and e-commerce websites has led to an increase in user-generated content, including product reviews. However, this surge in online reviews has also given rise to the issue of fake reviews, which can mislead consumers and undermine the credibility of online review systems. In response, automated fake review detection techniques have emerged as a promising solution to identify and mitigate fraudulent behavior. This paper provides a comprehensive review of recent advancements in automated fake review detection, including machine learning-based approaches, natural language processing (NLP) techniques, and deep learning models. Additionally, the paper discusses the challenges associated with automated fake review detection, such as adversarial attacks, data scarcity, domain generalization, and ethical considerations. By addressing these challenges and leveraging innovative techniques, automated fake review detection systems can enhance trust and transparency in online review platforms.