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

TL;DR

This study investigates how cultural differences, specifically individualism versus collectivism, influence linguistic cues in automatic deception detection across multiple languages and datasets, highlighting the complexity and need for culturally aware models.

Contribution

The paper introduces a cross-cultural analysis of deception detection in text, demonstrating that linguistic cues vary with culture and that universal models are insufficient without cultural adaptation.

Findings

Pronoun usage and sentiment expression are culturally influenced in deceptive language.

Cross-cultural deception detection models perform better when incorporating cultural cues.

Deception detection across cultures requires tailored approaches rather than a universal model.

Abstract

Deception detection is a task with many applications both in direct physical and in computer-mediated communication. Our focus is on automatic deception detection in text across cultures. 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 norms in respect to the individualism/collectivism divide. We also 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 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 11 datasets from 5 languages i.e., English, Dutch, Russian, Spanish and Romanian, from six countries (US, Belgium, India, Russia, Mexico and Romania), and we applied two classification methods i.e, logistic regression and fine-tuned BERT models. The results showed that our task is fairly complex and demanding. There are indications that some linguistic cues of deception have cultural origins, and are consistent in the context of diverse domains and dataset 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 a unified manner, and that such approaches should be augmented with knowledge about cultural differences and the domains of interest.