Show simple item record

dc.contributor.authorGupta, Smita
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2007-11-28 12:23:31.38en
dc.date2007-11-28 18:10:30.45en
dc.date.accessioned2007-11-29T20:00:00Z
dc.date.available2007-11-29T20:00:00Z
dc.date.issued2007-11-29T20:00:00Z
dc.identifier.urihttp://hdl.handle.net/1974/922
dc.descriptionThesis (Master, Computing) -- Queen's University, 2007-11-28 18:10:30.45en
dc.description.abstractAs organizations and government agencies work diligently to detect financial irregularities, malfeasance, fraud and criminal activities through intercepted communication, there is an increasing interest in devising an automated model/tool for deception detection. We build on Pennebaker's empirical model which suggests that deception in text leaves a linguistic signature characterised by changes in frequency of four categories of words: first-person pronouns, exclusive words, negative emotion words, and action words. By applying the model to the Enron email dataset and using an unsupervised matrix-decomposition technique, we explore the differential use of these cue-words/categories in deception detection. Instead of focusing on the predictive power of the individual cue-words, we construct a descriptive model which helps us to understand the multivariate profile of deception based on several linguistic dimensions and highlights the qualitative differences between deceptive and truthful communication. This descriptive model can not only help detect unusual and deceptive communication, but also possibly rank messages along a scale of relative deceptiveness (for instance from strategic negotiation and spin to deception and blatant lying). The model is unintrusive, requires minimal human intervention and, by following the defined pre-processing steps it may be applied to new datasets from different domains.en
dc.format.extent12133825 bytes
dc.format.mimetypeapplication/pdf
dc.languageenen
dc.language.isoenen
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.subjectDeception Detectionen
dc.subjectFrauden
dc.subjectText miningen
dc.subjectEmailen
dc.subjectEnronen
dc.titleModelling Deception Detection in Texten
dc.typeThesisen
dc.description.degreeMasteren
dc.contributor.supervisorSkillicorn, B. Daviden
dc.contributor.departmentComputingen


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record