Fake Reviews Detection through Ensemble Learning

TL;DR

This paper evaluates ensemble learning methods for detecting fake online reviews, demonstrating they outperform traditional machine learning techniques in identifying deceptive restaurant reviews.

Contribution

It provides an empirical comparison showing ensemble learning approaches are more effective than conventional methods for fake review detection.

Findings

Ensemble learning approaches outperform traditional algorithms.

Ensemble methods improve accuracy in fake review detection.

Study conducted on a custom dataset of restaurant reviews.

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.