# Deception detection in text and its relation to the cultural dimension of individualism/collectivism arXiv: https://arxiv.org/abs/2105.12530 ยท HTML: https://mlsanity.com/paper/2105.12530/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Builds on 32 of its 194 references that are in the map. - First posted on arXiv 26 May 2021. ## Builds on (most cited first, 30 of 32) - [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018) - [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019) - [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019) - [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019) - [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013) - [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019) - [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016) - [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019) - [XNLI: Evaluating Cross-lingual Sentence Representations](https://mlsanity.com/paper/1809.05053/) (2018) - [A Primer in BERTology: What we know about how BERT works](https://mlsanity.com/paper/2002.12327/) (2020) - [XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization](https://mlsanity.com/paper/2003.11080/) (2020) - [Distributed Representations of Sentences and Documents](https://mlsanity.com/paper/1405.4053/) (2014) - [How multilingual is Multilingual BERT?](https://mlsanity.com/paper/1906.01502/) (2019) - [Language-agnostic BERT Sentence Embedding](https://mlsanity.com/paper/2007.01852/) (2020) - [Massively Multilingual Sentence Embeddings for Zero-Shot Cross-Lingual Transfer and Beyond](https://mlsanity.com/paper/1812.10464/) (2018) - [Neural Legal Judgment Prediction in English](https://mlsanity.com/paper/1906.02059/) (2019) - [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019) - [A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios](https://mlsanity.com/paper/2010.12309/) (2020) - [Adaptation of Deep Bidirectional Multilingual Transformers for Russian Language](https://mlsanity.com/paper/1905.07213/) (2019) - [BERTje: A Dutch BERT Model](https://mlsanity.com/paper/1912.09582/) (2019) - [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017) - [Predicting Factuality of Reporting and Bias of News Media Sources](https://mlsanity.com/paper/1810.01765/) (2018) - [How Language-Neutral is Multilingual BERT?](https://mlsanity.com/paper/1911.03310/) (2019) - [Learning Cross-Lingual Sentence Representations via a Multi-task Dual-Encoder Model](https://mlsanity.com/paper/1810.12836/) (2018) - [A Survey on Transfer Learning in Natural Language Processing](https://mlsanity.com/paper/2007.04239/) (2020) - [Learning Hierarchical Discourse-level Structure for Fake News Detection](https://mlsanity.com/paper/1903.07389/) (2019) - [Audio-Linguistic Embeddings for Spoken Sentences](https://mlsanity.com/paper/1902.07817/) (2019) - [A Deep Learning Approach for Multimodal Deception Detection](https://mlsanity.com/paper/1803.00344/) (2018) - [Detecting Deceptive Reviews using Generative Adversarial Networks](https://mlsanity.com/paper/1805.10364/) (2018) - [Introducing RONEC -- the Romanian Named Entity Corpus](https://mlsanity.com/paper/1909.01247/) (2019) ## Built on by (most cited first, 3 of 3) - [Linguistic Cues of Deception in a Multilingual April Fools' Day Context](https://mlsanity.com/paper/2111.03913/) (2021) - [Semantics of Subterfuge: Benchmarking Legal Deception Detection Against General-domain State-of-the-Art](https://mlsanity.com/paper/2607.29066/) (2026) - [A persistent accuracy ceiling in automated verbal deception detection](https://mlsanity.com/paper/2610.12118/) (2026) ## Abstract (arXiv) 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.