Year: 2020
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Gutierrez-Espinoza, Luis, Abri, Faranak, Namin, Akbar Siami, Jones, Keith S., Sears, David R. W.
Language: English
Topic: Spam and Phishing Detection
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3034338377