Detecting Deception in Interrogation Settings

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Lamb, Carolyn
Data Mining , Deception , Text Mining
Bag-of-words deception detection systems outperform humans, but are still not always accurate enough to be useful. In interrogation settings, present models do not take into account potential influence of the words in a question on the words in the answer. According to the theory of verbal mimicry, this ought to exist. We show with our research that it does exist: certain words in a question can "prompt" other words in the answer. However, the effect is receiver-state-dependent. Deceptive and truthful subjects in archival data respond to prompting in different ways. We can improve the accuracy of a bag-of-words deception model by training a machine learning algorithm on both question words and answer words, allowing it to pick up on differences in the relationships between these words. This approach should generalize to other bag-of-words models of psychological states in dialogues.
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