Paper: Cross-cultural Deception Detection

ACL ID P14-2072
Title Cross-cultural Deception Detection
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2014
Authors

In this paper, we address the task of cross-cultural deception detection. Using crowdsourcing, we collect three deception datasets, two in English (one originating from United States and one from India), and one in Spanish obtained from speakers from Mexico. We run comparative experi- ments to evaluate the accuracies of decep- tion classifiers built for each culture, and also to analyze classification differences within and across cultures. Our results show that we can leverage cross-cultural information, either through translation or equivalent semantic categories, and build deception classifiers with a performance ranging between 60-70%.