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Deception detection is a task with many applications both in direct physicaland in computer-mediated communication. Our focus is on automatic deceptiondetection in text across cultures. We view culture through the prism of theindividualism/collectivism dimension and we approximate culture by usingcountry as a proxy. Having as a starting point recent conclusions drawn fromthe social psychology discipline, we explore if differences in the usage ofspecific linguistic features of deception across cultures can be confirmed andattributed to norms in respect to the individualism/collectivism divide. Wealso investigate if a universal feature set for cross-cultural text deceptiondetection tasks exists. We evaluate the predictive power of different featuresets and approaches. We create culture/language-aware classifiers byexperimenting with a wide range of n-gram features based on phonology,morphology and syntax, other linguistic cues like word and phoneme counts,pronouns use, etc., and token embeddings. We conducted our experiments over 11datasets from 5 languages i.e., English, Dutch, Russian, Spanish and Romanian,from six countries (US, Belgium, India, Russia, Mexico and Romania), and weapplied two classification methods i.e, logistic regression and fine-tuned BERTmodels. The results showed that our task is fairly complex and demanding. Thereare indications that some linguistic cues of deception have cultural origins,and are consistent in the context of diverse domains and dataset settings forthe same language. This is more evident for the usage of pronouns and theexpression of sentiment in deceptive language. The results of this work showthat the automatic deception detection across cultures and languages cannot behandled in a unified manner, and that such approaches should be augmented withknowledge about cultural differences and the domains of interest.

Deception detection in text and its relation to the cultural dimension of individualism/collectivism