Using linguistically-defined specific details to detect deception across domains

Current automatic deception detection approaches tend to rely on cues that are based either on specific lexical items or on linguistically abstract features that are not necessarily motivated by the psychology of deception. Notably, while approaches relying on such features can do well when the content domain is similar for training and testing, they suffer when content changes occur. We investigate new linguistically-defined features that aim to capture specific details, a psychologically-motivated aspect of truthful vs. deceptive language that may be distinctive across content domains. To ascertain the potential utility of these features, we evaluate them on datasets representing a broad sample of deceptive language, including hotel reviews, opinions about emotionally-charged topics, and answers to job interview questions. We additionally evaluate these features as part of a deception detection classifier. We find that these linguistically-defined specific detail features are most useful for cross-domain deception detection when the training data differ significantly in content from the test data, and particularly benefit classification accuracy on deceptive documents. We discuss implications of our results for general-purpose approaches to deception detection.