26 May 2014, 11:00AM

Abstract: In this talk, we propose to use Rhetorical Structure Theory (RST) analytic framework to identify systematic differences between deceptive and truthful stories in terms of their coherence and structure. Sample stories are self-ranked by the study participants as completely truthful or completely deceptive. Research analysts manually assign RST discourse relations among each story's constituent parts. Vector Space Model (VSM) assesses each story's position in multi-dimensional RST space with respect to its distance to truth and deceptive centers as measures of the story's level of deception and truthfulness. Human judges further evaluate whether each story is deceptive or not, and assign their confidence levels, which produce measures of the human expected deception and truthfulness levels. Our contributions are twofold. First, the developed RST-VSM methodology interprets RST analysis in identification of previously unseen deceptive texts. Second, this reserach demonstrates that discourse structure analysis in pragmatics is a promising venue for automated deception detection and, as such, an effective complement to lexico-semantic analysis

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