Abstract
AbstractAutomatic deception detection is a crucial task that has many applications both in direct physical and in computer-mediated human communication. Our focus is on automatic deception detection in text across cultures. In this context, we view culture through the prism of the individualism/collectivism dimension, and we approximate culture by using country as a proxy. Having as a starting point recent conclusions drawn from the social psychology discipline, we explore if differences in the usage of specific linguistic features of deception across cultures can be confirmed and attributed to cultural norms in respect to the individualism/collectivism divide. In addition, we investigate if a universal feature set for cross-cultural text deception detection tasks exists. We evaluate the predictive power of different feature sets and approaches. We create culture/language-aware classifiers by experimenting with a wide range of n-gram features from several levels of linguistic analysis, namely phonology, morphology and syntax, other linguistic cues like word and phoneme counts, pronouns use, etc., and token embeddings. We conducted our experiments over eleven data sets from five languages (English, Dutch, Russian, Spanish, and Romanian), from six countries (United States of America, Belgium, India, Russia, Mexico, and Romania), and we applied two classification methods, namely logistic regression and fine-tuned BERT models. The results showed that the undertaken task is fairly complex and demanding. Furthermore, there are indications that some linguistic cues of deception have cultural origins and are consistent in the context of diverse domains and data set settings for the same language. This is more evident for the usage of pronouns and the expression of sentiment in deceptive language. The results of this work show that the automatic deception detection across cultures and languages cannot be handled in unified manners and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.
Highlights
Automated deception detection builds on years of research in interpersonal psychology, philosophy, sociology, communication studies, and computational models of deception detection (Vrij 2008a; Granhag et al 2014)
An interesting point is that for the Bluff data set, the plain BERT model offers better performance to the logistic classifier (83% accuracy compared to 75%), which drops to 77% when combined with the linguistic features
This study explores the task of automated text-based deception detection within cultures by taking into consideration cultural and language factors, as well as limitations in NLP tools and resources for the examined cases
Summary
Automated deception detection builds on years of research in interpersonal psychology, philosophy, sociology, communication studies, and computational models of deception detection (Vrij 2008a; Granhag et al 2014). The most influential theory that connects specific linguistic cues with the truthfulness of a statement is the Undeutsch hypothesis (Undeutsch 1967; Undeutsch 1989) This hypothesis asserts that statements of real-life experiences derived from memory differ significantly in content and quality from fabricated ones, since the invention of a fictitious memory requires more cognitive creativity and control than remembering an experienced event. On this basis, a great volume of research work examines which linguistic features are more suitable to distinguish a truthful from a deceptive statement.
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