  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.
