TY - GEN
T1 - Detecting deception using machine learning
AU - Delgado, Alberto Alejandro Ceballos
AU - Glisson, William Bradly
AU - Shashidhar, Narasimha
AU - McDonald, J. Todd
AU - Grispos, George
AU - Benton, Ryan
N1 - Publisher Copyright:
© 2021 IEEE Computer Society. All rights reserved.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
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M3 - Conference contribution
AN - SCOPUS:85108375659
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 7122
EP - 7131
BT - Proceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
A2 - Bui, Tung X.
PB - IEEE Computer Society
T2 - 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
Y2 - 4 January 2021 through 8 January 2021
ER -