A Survey on Review Spam Detection Methods using Deep Learning Approach

Document Type : Original Article

Authors

1 Master of Information Technology Engineering, Deep Learning Research Lab, Faculty of Engineering, College of Farabi, University of Tehran, Iran

2 Assistant Professor, Department of Computer Engineering, Faculty of Engineering, College of Farabi, University of Tehran, Iran; kfouladi@ut.ac.ir

Abstract

Review spam is an opinion written to promote or demote a product or brand on websites and other internet services by some users. Since it is not easy for humans to recognize these types of opinions, a model can be provided to detect them. In recent years, much research has been done to detect these types of reviews, and with the expansion of deep neural networks and the efficiency of these networks in various issues, in recent years, multiple types of deep neural networks have been used to identify spam reviews. This paper reviews the proposed deep learning methods for the problem of review spam detection. Challenges, evaluation criteria, and datasets in this area are also examined.

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Main Subjects

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  •  Mahmoud Aliarab received the B.Sc. degree in Computer Engineering from Shahid Beheshti University in 2018, and M.Sc. degree in Information Technology Engineering from University of Tehran in 2021. He currently works at Deep Learning Research Lap, University of Tehran. His current research interests include deep learning, natural language processing and data mining.
  •  Kazim Fouladi-Ghaleh received the M.Sc. and Ph.D. degrees in Electrical and Computer Engineering / Artificial Intelligence and Robotics from School of ECE, College of Engineering, University of Tehran, Iran. He is currently an assistant professor of Computer Engineering at University of Tehran, College of Farabi, Faculty of Engineering and the director of the Cyberspace Research Lab and Deep Learning Research Lab. His research interests include AI, Image Processing & Computer Vision, Deep Learning, Cybernetics and Cyberspace Studies.
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International Journal of Web Research
Volume 5, Issue 1
Winter-Spring
June 2022
Pages 19-24

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