Liar Detector Trained by Japanese Language Expressions and Validation of Domain Independence

Abstract

The paper discusses the challenges of detecting deception in text communication, where non-verbal cues like tone of voice and facial expressions are absent, making it more difficult than in-person communication. It addresses the issue of scarcity in labeled corpora for lie detection research. The study uses data from a Werewolf BBS (bulletin board system), which, while extensive and useful, might be too specific to the Werewolf game context in terms of vocabulary and expressions. To address this, the research filters data from a general domain, X's posting data, using only common vocabulary to enhance generalizability. Additionally, a new dataset for a murder mystery game was created to further validate the model trained on Werewolf BBS logs for cross-domain applicability. Experiments in lie detection were conducted using several different learning methods, with the BERT-GRU hierarchical model achieving an F1 score of 0.70 before filtering and 0.68 after, demonstrating the feasibility of lie detection in general domains.