Fake Reviews Detection through Ensemble Learning

• URL: http://arxiv.org/abs/2006.07912v1
• Date: Sun, 14 Jun 2020 14:24:02 GMT
• Title: Fake Reviews Detection through Ensemble Learning
• Authors: Luis Gutierrez-Espinoza and Faranak Abri and Akbar Siami Namin and Keith S. Jones and David R. W. Sears
• Abstract summary: Several machine learning-based approaches can automatically detect deceptive and fake reviews. This paper evaluates the performance of ensemble learning-based approaches to identify bogus online information.
• Score: 1.609940380983903
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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