  People are regularly confronted with potentially deceptive statements (e.g.,
fake news, misleading product reviews, or lies about activities). Only few
works on automated text-based deception detection have exploited the potential
of deep learning approaches. A critique of deep-learning methods is their lack
of interpretability, preventing us from understanding the underlying
(linguistic) mechanisms involved in deception. However, recent advancements
have made it possible to explain some aspects of such models. This paper
proposes and evaluates six deep-learning models, including combinations of BERT
(and RoBERTa), MultiHead Attention, co-attentions, and transformers. To
understand how the models reach their decisions, we then examine the model's
predictions with LIME. We then zoom in on vocabulary uniqueness and the
correlation of LIWC categories with the outcome class (truthful vs deceptive).
The findings suggest that our transformer-based models can enhance automated
deception detection performances (+2.11% in accuracy) and show significant
differences pertinent to the usage of LIWC features in truthful and deceptive
statements.
