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Analysis of concept drift in fake reviews detection
journal contribution
posted on 2023-05-20, 23:14 authored by Rami Mohawesh, Son TranSon Tran, Robert OllingtonRobert Ollington, Shuxiang XuShuxiang XuOnline reviews have a substantial impact on decision making in various areas of society, predominantly in the arena of buying and selling of goods. As such, the truthfulness of internet reviews is critical for both consumers and vendors. Fake reviews not only mislead innocent clients and influence customers' choice, leading to inaccurate descriptions and sales. This raises the need for efficient fake review detection models and tools that can address these issues. Analysing a text data stream of fake reviews in concept drift appears to reduce the effectiveness of the detection models. Despite several efforts to develop algorithms for detecting fake reviews, one crucial aspect that has not been addressed is finding a real correlation between the concept drift score and the classification of performance over-time in the real-world data stream. Consequently, we have introduced a comprehensive analysis to investigate the concept drift problem within fake review detection. There are two methods to achieve this goal: benchmarking concept drift detection method and content-based classification methods. We conducted our experiment using four real-world datasets from Yelp.com. The results demonstrated that there is a strong negative correlation between concept drift and the performance of fake review detection/prediction models, which indicates the difficulty of building more efficient models.
History
Publication title
Expert Systems with ApplicationsVolume
169Article number
114318Number
114318Pagination
1-20ISSN
0957-4174Department/School
School of Information and Communication TechnologyPublisher
Pergamon-Elsevier Science LtdPlace of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Ox5 1GbRights statement
Copyright 2020 Elsevier Ltd. All rights reserved.Repository Status
- Restricted