# Fake Reviews Detection through Ensemble Learning

arXiv: https://arxiv.org/abs/2006.07912 · HTML: https://mlsanity.com/paper/2006.07912/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [Rumor Classification through a Multimodal Fusion Framework and Ensemble Learning](https://mlsanity.com/paper/2302.05289/) (2023)

## Abstract (arXiv)

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.