  Deception, a prevalent aspect of human communication, has undergone a
significant transformation in the digital age. With the globalization of online
interactions, individuals are communicating in multiple languages and mixing
languages on social media, with varied data becoming available in each language
and dialect. At the same time, the techniques for detecting deception are
similar across the board. Recent studies have shown the possibility of the
existence of universal linguistic cues to deception across domains within the
English language; however, the existence of such cues in other languages
remains unknown. Furthermore, the practical task of deception detection in
low-resource languages is not a well-studied problem due to the lack of labeled
data. Another dimension of deception is multimodality. For example, a picture
with an altered caption in fake news or disinformation may exist. This paper
calls for a comprehensive investigation into the complexities of deceptive
language across linguistic boundaries and modalities within the realm of
computer security and natural language processing and the possibility of using
multilingual transformer models and labeled data in various languages to
universally address the task of deception detection.
