Detecting deception in interrogation settings

Bag-of-words deception detection systems outperform humans, but are still not always accurate enough to be useful. In interrogation settings, the language of questions influences the language of responses. We develop a technique to correct for such influences and apply it to question-and-answer datasets. Surprisingly, such correction is not sufficient - those being deceptive react to prompting words in qualitatively different ways to those telling the truth. Accurate detection of deception in interrogation settings therefore requires modelling word use in both questions and answers.