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dc.contributor.authorLamb, Carolynen
dc.date2012-12-14 11:18:13.823
dc.date2012-12-17 14:42:19.707
dc.date.accessioned2012-12-18T14:59:06Z
dc.date.available2012-12-18T14:59:06Z
dc.date.issued2012-12-18
dc.identifier.urihttp://hdl.handle.net/1974/7695
dc.descriptionThesis (Master, Computing) -- Queen's University, 2012-12-17 14:42:19.707en
dc.description.abstractBag-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.en
dc.language.isoengen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectData Miningen
dc.subjectDeceptionen
dc.subjectText Miningen
dc.titleDetecting Deception in Interrogation Settingsen
dc.typethesisen
dc.description.degreeM.Sc.en
dc.contributor.supervisorSkillicorn, B. Daviden
dc.contributor.departmentComputingen
dc.degree.grantorQueen's University at Kingstonen


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