Resilience of Society to Recognize Disinformation: Human and/or Machine Intelligence

The paper conceptualizes the societal impacts of disinformation in hopes of developing a computational approach that can identify disinformation in order to strengthen social resilience. An innovative approach that considers the sociotechnical interaction phenomena of social media is utilized to address and combat disinformation campaigns. Based on theoretical inquiries, this study proposes conducting experiments that capture subjective and objective measures and datasets while adopting machine learning to model how disinformation can be identified computationally. The study particularly will focus on understanding communicative social actions as human intelligence when developing machine intelligence to learn about disinformation that is deliberately misleading, as well as the ways people judge the credibility and truthfulness of information. Previous experiments support the viability of a sociotechnical approach, i.e., connecting subtle language-action cues and linguistic features from human communication with hidden intentions, thus leading to deception detection in online communication. The study intends to derive a baseline dataset and a predictive model and by that to create an information system artefact with the capability to differentiate disinformation

Computer-Mediated Deception: Collective Language-action Cues as Stigmergic Signals for Computational Intelligence

Collective intelligence is easily observable in group-based or interpersonal pairwise interaction, and is enabled by environment-mediated stigmertic 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 stigmertic 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