Extending Textual Models of Deception to Interrogation Settings

David Skillicorn, Carolyn Lamb


Models that detect deception in text typically outperform humans but are limited to single pieces of text created by a single individual. Text from dialogues and wider conversations reflects linguistic influence among the participants, and this intertwining makes it difficult to ascribe deception to any one of them. We address this problem in dialogues, particularly interrogations. by seeking to detect and remove the influence of the language of a question from the language of the response. Surprisingly, this does not work as expected: the response by a deceptive person to certain categories of words in questions is qualitatively different from that of a truthful person. Successful prediction of deception in responses, therefore, requires analysis using the words of both questions and answers. We show that such prediction is indeed effective.


deception detection, dialogue, discourse structure

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DOI: https://doi.org/10.5195/lesli.2013.1


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