Deception detection methods incorporating discourse network metrics in synchronous computer-mediated communication

Abstract

The prevalence of deception in computer-mediated communication and the risk of misjudgement based on deceptive information call for effective detection methods of deception. Extant models for online deception detection rely mainly on verbal behaviours of participants while largely ignoring context. Discourse behaviour analysis, which can better investigate the information in context, has been proved effective for online deception detection; nevertheless, these discourse behaviours have been analysed in isolation without referring to other behaviours in context. To achieve the ultimate goal of effective prediction of deception in synchronous computer-mediated communication, this research exploits temporal networks in uncovering the dynamics of deception behaviours, proposes novel deception detection methods using discourse network metrics as predictive features, and empirically evaluates the performances of deception detection methods incorporating three types of predictive features (non-discourse features, discourse features and discourse network metrics). The results suggest that discourse network features are more effective in detecting deception and incorporating these features with non-discourse and discourse features can significantly improve the performance of deception detection. The findings not only demonstrate the efficacy of structural features in deception detection but also offer both methodological and theoretical contributions to deception detection from the perspective of temporal network.