Computer-Mediated Deception: Collective Language-action Cues as Stigmergic Signals for Computational Intelligence
Abstract
Collective intelligence is easily observable in group-based or interpersonal pairwise interaction, and is enabled by environment-mediated stigmergic signals. Based on innate ability, human sensors not only sense and coordinate, but also tend to solve problems through these signals. This paper argues the efficacy of computational intelligence for adopting the collective language-action cues of human intelligence as stigmergic signals to differentiate deception. A study was conducted in synchronous computer-mediated communication environment with a dataset collected from 2014 to 2015. An online game was developed to examine the accuracy of certain language-action cues (signs), deceptive actors (agents) during pairwise interaction (environment). The result of a logistic regression analysis demonstrates the computational efficacy of collective language-action cues in differentiating and sensing deception in spontaneous communication. This study contributes to the computational modeling in adapting human intelligence as a base to attribute computer-mediated deception.