A comprehensive survey of fake review detection technology

Quyyam, Tayybaha and Qicheng, Yu (2024) A comprehensive survey of fake review detection technology. In: 12th International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA-2024), 6-7 June 2024, London Metropolitan University, London (UK) / Online. (In Press)

Abstract

Fake reviews (FR) can damage a company's reputation and cause consumers to purchase low-value products and services. With the advancement of artificial intelligence, FRD technology and its detection accuracy have improved significantly. To seek state-of-the-art FRD technology, this paper will conduct a systematic literature survey to explore the solutions of scholars working on methods to effectively detect FR, unsolved problems in this field, and future research directions. The survey covered 30 recent research papers between 2019 and 2023. Our findings are categorized as machine learning, deep learning, and hybrid methods to provide researchers and experts with an outlook on proposed solutions and their limitations. The survey also offers future research directions and an easy way to find datasets, carry out pre-processing, and extract multiple features. One of the main directions in the future is to combine review content with business, product, and reviewer behavior to improve the efficiency of FRD.

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