Ensemble Learning for Detecting Fake Reviews

Luis Gutierrez-Espinoza, Faranak Abri, Akbar Siami Namin, Keith S. Jones, David R.W. Sears

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020
EditorsW. K. Chan, Bill Claycomb, Hiroki Takakura, Ji-Jiang Yang, Yuuichi Teranishi, Dave Towey, Sergio Segura, Hossain Shahriar, Sorel Reisman, Sheikh Iqbal Ahamed
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1320-1325
Number of pages6
ISBN (Electronic)9781728173030
DOIs
StatePublished - Jul 2020
Event44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020 - Virtual, Madrid, Spain
Duration: Jul 13 2020Jul 17 2020

Publication series

NameProceedings - 2020 IEEE 44th Annual Computers, Software, and Applications Conference, COMPSAC 2020

Conference

Conference44th IEEE Annual Computers, Software, and Applications Conference, COMPSAC 2020
Country/TerritorySpain
CityVirtual, Madrid
Period07/13/2007/17/20

Keywords

  • Ensemble learning
  • deception detection

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