Classification of real and falsified narratives in a repeated-measures text corpus
department or school: Arts and Sciences, Irving K. Barber School of (Okanagan)
department or school: Psychology, Department of (Okanagan)
data provider: DSpace
degree campus: UBCO
creator: Ran, Wei
issued: 2020-01-09T23:37:06Z
issued: 2020
related degree: Doctor of Philosophy - PhD
degree grantor: University of British Columbia
description: A great deal of research has been devoted to finding reliable ways of detecting deception. The current deception literature has recognized that human deceptive behavior is highly complex; behavioral differences due to deception (deception cues) are small and probably context dependent. In general, using a repeated-measure design to control variabilities at the individual level is an effective way of amplifying small behavioral effects; however, no study so far has explored the benefit of adding individual-level random-effects in deception detection models. This dissertation focused on a developing field of language-based deception detection research that utilizes natural language processing (NLP; e.g., Fitzpatrick, Bachenko, & Fornaciari, 2015; Heydari, Tavakolia, Salima, & Heydari, 2015). We tested a novel NLP-based deception detection scheme that utilizes multiple language samples from the same individual. A repeated-measures truthful-and-fabricated text corpus (4 truthful and 4 fabricated statements per individual) from 152 individuals was collected. Truth-telling and fabrication scenarios were created using video recordings of real-life negative events as stimuli. Various sets of cues including n-grams, POS tags, and psycholinguistic cues were extracted from the text sample using NLP techniques. Our results showed that mixed-effects variations of popular classification models including logistic regression, decision tree/random forest, and artificial neural network have better cross-context generalizability than their regular fixed-effects counterparts. This research should encourage further development of repeated-measure deception detection schemes and classification models that can fully utilize such a data structure.
aggregated CHO: https://circle.library.ubc.ca/rest/handle/2429/73252
note: CLASSIFICATION OF REAL AND FALSIFIED NARRATIVES IN A REPEATED-MEASURES TEXT CORPUS by Ran Wei M.A., The University of New Brunswick, 2013 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE COLLEGE OF GRADUATE STUDIES (Psychology) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) January 2020 © Ran Wei, 2020 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, the dissertation entitled: CLASSIFICATION OF REAL AND FALSIFIED NARRATIVES IN A REPEATED-MEASURES TEXT CORPUS submitted by Ran Wei in partial fulfillment of the requirements for the degree of Doctor of Philosophy Examining Committee: Brian OâConnor, Irving K. Barber School of Arts and Sciences, University of British Columbia â Okanagan Supervisor Mike Woodworth, Irving K. Barber School of Arts and Sciences, University of British Columbia â Okanagan Supervisory Committee Member Jan Cioe, Irving K. Barber School of Arts and Sciences, University of British Columbia â Okanagan Supervisory Committee Member Fatemeh Fard, Irving K. Barber School of Arts and Sciences, University of British Columbia â Okanagan University Examiner Doug Twitchell, College of Business and Economics, Boise State University External Examiner iii Abstract A great deal of research has been devoted to finding reliable ways of detecting deception. The current deception literature has recognized that human deceptive behavior is highly complex; behavioral differences due to deception (deception cues) are small and probably context dependent. In general, using a repeated-measure design to control variabilities at the individual level is an effective way of amplifying small behavioral effects; however, no study so far has explored the benefit of adding individual-level random-effects in deception detection models. This dissertation focused on a developing field of language-based deception detection research that utilizes …
has type: Thesis/Dissertation
date issued: 2020-02
is shown at: 10.14288/1.0388225
language: eng
degree discipline: Psychology
provider: Vancouver : University of British Columbia Library
publisher: University of British Columbia
rights: Attribution-NonCommercial-NoDerivatives 4.0 International
rights URI: http://creativecommons.org/licenses/by-nc-nd/4.0/
scholar level: Graduate
type: Text
identifier URI: http://hdl.handle.net/2429/73252