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.
@article{arxiv.2006.07912,
title = {Fake Reviews Detection through Ensemble Learning},
author = {Luis Gutierrez-Espinoza and Faranak Abri and Akbar Siami Namin and Keith S. Jones and David R. W. Sears},
journal= {arXiv preprint arXiv:2006.07912},
year = {2020}
}