conference paper

Using Timer Data to Conjunct Self-Reported Measures in Quantifying Deception

Caramancion K.M.

Publication year: 2022
Source / journal: 2022 ieee world ai iot congress aiiot 2022
Country of origin: USA
Language: English
Authors’ affiliations: University at Albany, State University of New York, College of Emergency Preparedness, Albany, NY, United States
Import source: Scopus export

Abstract

This paper proposes the reduction of self-reported measures in deception quantifying assessment psychometric devices through the integration of digital trace data to limit bias and cognitive laziness. This paper then proceeded to test this proposal through a simulation involving a user study where a previous work's predictive model was recreated to incorporate such changes. The results highly suggest that the conjunction of digital trace data yields lower unaccounted variance (i.e., noise) and stronger forecasting prowess of the predictive models. The intended target audiences of this paper are information scientists, digital forensic professionals, communication experts, and policymakers possibly seeking references in this application area. copy, 2022 IEEE. © 2022 IEEE.

Author keywords

cyber deception, disinformation, fake news, information warfare, misinformation

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