Paper: Syntactic Stylometry for Deception Detection

ACL ID P12-2034
Title Syntactic Stylometry for Deception Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2012

Most previous studies in computerized de- ception detection have relied only on shal- low lexico-syntactic patterns. This pa- per investigates syntactic stylometry for deception detection, adding a somewhat unconventional angle to prior literature. Over four different datasets spanning from the product review to the essay domain, we demonstrate that features driven from Context Free Grammar (CFG) parse trees consistently improve the detection perfor- mance over several baselines that are based only on shallow lexico-syntactic features. Our results improve the best published re- sult on the hotel review data (Ott et al., 2011) reaching 91.2% accuracy with 14% error reduction.