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Fake Reviews Detection through Ensemble Learning

14 June 2020
Luis Gutiérrez-Espinoza
Faranak Abri
A. Namin
Keith S. Jones
David R. W. Sears
    AAML
ArXiv (abs)PDFHTML
Abstract

Customers represent their satisfactions of consuming products by sharing their experiences through the utilization of online reviews. Several machine learning-based approaches can automatically detect deceptive and fake reviews. Recently, there have been studies reporting the performance of ensemble learning-based approaches in comparison to conventional machine learning techniques. Motivated by the recent trends in ensemble learning, this paper evaluates the performance of ensemble learning-based approaches to identify bogus online information. The application of a number of ensemble learning-based approaches to a collection of fake restaurant reviews that we developed show that these ensemble learning-based approaches detect deceptive information better than conventional machine learning algorithms.

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