Truth and deception at the rhetorical structure level

Abstract

This paper furthers the development of methods to distinguish truth from deception in textual data. We use rhetorical structure theory (RST) as the analytic framework to identify systematic differences between deceptive and truthful stories in terms of their coherence and structure. A sample of 36 elicited personal stories, self-ranked as truthful or deceptive, is manually analyzed by assigning RST discourse relations among each story's constituent parts. A vector space model (VSM) assesses each story's position in multidimensional RST space with respect to its distance from truthful and deceptive centers as measures of the story's level of deception and truthfulness. Ten human judges evaluate independently whether each story is deceptive and assign their confidence levels (360 evaluations total), producing measures of the expected human ability to recognize deception. As a robustness check, a test sample of 18 truthful stories (with 180 additional evaluations) is used to determine the reliability of our RST-VSM method in determining deception. The contribution is in demonstration of the discourse structure analysis as a significant method for automated deception detection and an effective complement to lexicosemantic analysis. The potential is in developing novel discourse-based tools to alert information users to potential deception in computermediated texts.

TALIP Perspectives, Guest Editorial Commentary

“…Most deception detection mechanisms operate at the levels of lexico-semantic analysis in combination with machine learning. At a pragmatic discourse level, automated methods have been attempted in very few studies thus far (see Bachenko et al [2008], Rubin and Lukoianova [2014], and Rubin and Vashchilko [2012]). What does an alternative pragmatic use of language imply for successful deception detection?…”

$$\mathsf {SAFE}$$: Similarity-Aware Multi-modal Fake News Detection

“…Instead of extracting features based on experience, Zhou et al [26] validated the role of fundamental theories in psychology and social science in guiding fake news feature engineering. Rhetorical structures among sentences or phrases within news content have also been investigated with either a vector space model [14] or Bi-LSTM [6]. Researchers have also explored the political bias [12] and homogeneity [2] of news publishers by mining news content that they have published, and have demonstrated how such information can help detect fake news.…”

Don't be deceived: Using linguistic analysis to learn how to discern online review authenticity

“…Another approach involves language‐based detection. It is hinged on the long‐known premise that authentic texts written based on experiences differ linguistically from fictitious texts concocted out of imagination (Johnson & Raye, ; Rubin & Lukoianova, ). Although the language of authentic and fictitious reviews could appear similar to the naked eye (DePaulo et al, ), linguistic nuances continue to be heralded as “the first thing to be considered” to automatically distinguish between authentic and fictitious reviews (Heydari et al, , p. 3635).…”