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Abstract

Online 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.

Item Type:	Article
Authors/Creators:	Mohawesh, R and Tran, S and Ollington, R and Xu, S
Keywords:	fake reviews, concept drift detectors, fake reviews detection techniques, time-changing data
Journal or Publication Title:	Expert Systems with Applications
Publisher:	Pergamon-Elsevier Science Ltd
ISSN:	0957-4174
DOI / ID Number:	10.1016/j.eswa.2020.114318
Copyright Information:	Copyright 2020 Elsevier Ltd. All rights reserved.
Related URLs:	Google Scholar: Ollington, R UTAS Profile: Ollington, R Google Scholar: Xu, S UTAS Profile: Xu, S
Item Statistics:	View statistics for this item

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