Unmasking the Deception: A Focused Survey of Machine Learning Techniques for Fake Review Detection

The expansion of false reviews on e-commerce sites turns out to be a significant issue for the integrity of user-generated content. False reviews may influence the customer’s choice and trustworthiness of sellers. Such reviews are hard to detect because of the absence of labeled data and evolving strategies of spammers. Subsequently, unsupervised learning has become a scalable solution for researchers to detect fake reviews. The paper constitutes a dedicated literature review of machine learning methods, including clustering, anomaly detection autoencoders and hybrid architectures. Supervised learning, Transformer based and deep learning architectures have also been discussed in the review. Based on this taxonomy and comparative analysis, this paper sheds light on the frequently used datasets, evaluation metrics and key trends. Finally, the review concludes with the discussion of the principal limitations and future directions, wherein the attention is paid to semi-supervised learning, multimodal data integration and more flexible and transparent models. The intent of the review is to act as a source of knowledge for the researchers who intend to contribute to the body of knowledge in the area of fake review identification by utilization of unsupervised learning and hybrid learning schemes.