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Seeing through deception: a computational approach to deceit detection in written communication

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dc.contributor.authorAlmela, Ángela
dc.contributor.authorValencia García, Rafael
dc.contributor.authorCantos Gómez, Pascual
dc.contributor.departmentFilología Inglesa
dc.contributor.departmentInformática y Sistemas
dc.date.accessioned2026-02-23T17:31:45Z
dc.date.available2026-02-23T17:31:45Z
dc.date.copyright© 2012 The Association for Computational Linguistics
dc.date.issued2012
dc.description.abstractThe present paper addresses the question of the nature of deception language. Specifically, the main aim of this piece of research is the exploration of deceit in Spanish written communication. We have designed an automatic classifier based on Support Vector Machines (SVM) for the identification of deception in an ad hoc opinion corpus. In order to test the effectiveness of the LIWC2001 categories in Spanish, we have drawn a comparison with a Bag-of-Words (BoW) model. The results indicate that the classification of the texts is more successful by means of our initial set of variables than with the latter system. These findings are potentially applicable to areas such as forensic linguistics and opinion mining, where extensive research on languages other than English isneeded.
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dc.format.extent8
dc.identifier.citationÁngela Almela, Rafael Valencia-García, and Pascual Cantos. 2012. Seeing through Deception: A Computational Approach to Deceit Detection in Written Communication. In Proceedings of the Workshop on Computational Approaches to Deception Detection, pages 15–22, Avignon, France.
dc.identifier.eisbn978-1-937284-19-0
dc.identifier.urihttp://hdl.handle.net/10201/211182
dc.languageeng
dc.publisherAssociation for Computational Linguistics
dc.relationThis work was supported by the Spanish Government through project SeCloud (TIN2010-18650). Ángela Almela was supported by Fundación Séneca scholarship 12406/FPI/09.
dc.relation.ispartofProceedings of the 13th Conference of the European Chapter of the Association for Computation Linguistics (Workshop on Computational Approaches to Deception Detection). Ed.: Association for Computational Linguistics, pp. 15-22
dc.relation.publisherversionhttp://www.aclweb.org/anthology/W12-0403
dc.rightsAttribution-NonCommercial-ShareAlike 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/*
dc.subjectNatural language processing
dc.subjectMachine learning
dc.subjectDeception detection
dc.subject.odsObjetivo 4: Educación
dc.titleSeeing through deception: a computational approach to deceit detection in written communication
dc.typeinfo:eu-repo/semantics/bookPart
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
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relation.isAuthorOfPublication.latestForDiscoverya3124e18-690d-4cfc-80a4-98e6b667d928
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