Detecting deceptive groups using conversations and network analysis

Abstract

Deception detection has been formulated as a supervised binary classification problem on single documents. However, in daily life, millions of fraud cases involve detailed conversations between deceivers and victims. Deceivers may dynamically adjust their deceptive statements according to the reactions of victims. In addition, people may form groups and collaborate to deceive others. In this paper, we seek to identify deceptive groups from their conversations. We propose a novel subgroup detection method that combines linguistic signals and signed network analysis for dynamic clustering. A social-elimination game called Killer Game is introduced as a case study1. Experimental results demonstrate that our approach significantly outperforms human voting and state-of-Theart subgroup detection methods at dynamically differentiating the deceptive groups from truth-Tellers.