  Verbal deception has been studied in psychology, forensics, and computational
linguistics for a variety of reasons, like understanding behaviour patterns,
identifying false testimonies, and detecting deception in online communication.
Varying motivations across research fields lead to differences in the domain
choices to study and in the conceptualization of deception, making it hard to
compare models and build robust deception detection systems for a given
language. With this paper, we improve this situation by surveying available
English deception datasets which include domains like social media reviews,
court testimonials, opinion statements on specific topics, and deceptive
dialogues from online strategy games. We consolidate these datasets into a
single unified corpus. Based on this resource, we conduct a correlation
analysis of linguistic cues of deception across datasets to understand the
differences and perform cross-corpus modeling experiments which show that a
cross-domain generalization is challenging to achieve. The unified deception
corpus (UNIDECOR) can be obtained from
https://www.ims.uni-stuttgart.de/data/unidecor.
