Detecting deception using machine learning

Alberto Alejandro Ceballos Delgado, William Bradly Glisson, Narasimha Shashidhar, J. Todd McDonald, George Grispos, Ryan Benton

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations


Today's digital society creates an environment potentially conducive to the exchange of deceptive information. The dissemination of misleading information can have severe consequences on society. This research investigates the possibility of using shared characteristics among reviews, news articles, and emails to detect deception in text-based communication using machine learning techniques. The experiment discussed in this paper examines the use of Bag of Words and Part of Speech tag features to detect deception on the aforementioned types of communication using Neural Networks, Support Vector Machine, Naïve Bayesian, Random Forest, Logistic Regression, and Decision Tree. The contribution of this paper is two-fold. First, it provides initial insight into the identification of text communication cues useful in detecting deception across different types of text-based communication. Second, it provides a foundation for future research involving the application of machine learning algorithms to detect deception on different types of text communication.

Original languageEnglish (US)
Title of host publicationProceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
EditorsTung X. Bui
PublisherIEEE Computer Society
Number of pages10
ISBN (Electronic)9780998133140
StatePublished - 2021
Event54th Annual Hawaii International Conference on System Sciences, HICSS 2021 - Virtual, Online
Duration: Jan 4 2021Jan 8 2021

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
ISSN (Print)1530-1605


Conference54th Annual Hawaii International Conference on System Sciences, HICSS 2021
CityVirtual, Online

ASJC Scopus subject areas

  • General Engineering


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