A Study on the Impact of Transfer Learning for Deception Detection

dc.contributor.advisorVerma, Rakesh M.
dc.contributor.committeeMemberShi, Weidong
dc.contributor.committeeMemberMarchette, David J.
dc.creatorTriplett, Steven Mark
dc.date.accessioned2024-01-20T21:14:39Z
dc.date.createdAugust 2023
dc.date.issued2023-08
dc.date.updated2024-01-20T21:14:39Z
dc.description.abstractIn the modern age, an enormous amount of communication occurs online, and it is difficult to know when something written is genuine or deceitful. There exist many reasons for someone to be less-than-truthful online (i.e., monetary gain, political gain), and identifying this behavior without any physical interaction is a difficult task. To address this, we utilize eight datasets from various domains to evaluate their effect on classifier performance when combined with transfer learning. We perform these experiments with multiple classifiers TFIDF features for classification and find that traditional classifiers suffer from a decrease in performance in almost all cases. Additionally, we generated text to evaluate transfer between a dataset similar to the target dataset and found that this improved BERT performance. Finally, we explored the effect that combining embeddings generated by separate BERT models fine-tuned on separate deception datasets has on performance and saw several examples of improvement in baseline accuracy. Furthermore, the effect of using multiple methods that add information to text via named entities was evaluated using a BERT model as well as a transfer learning method. We found that baseline BERT accuracy increased by up to 7.3%, with the most useful method replacing a named entity with its part-of-speech tag. Finally, we found that adding information via named entities consistently improved transfer learning accuracy for at least one method of adding information.
dc.description.departmentComputer Science, Department of
dc.format.digitalOriginborn digital
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10657/15943
dc.language.isoeng
dc.rightsThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).
dc.subjectDeception detection
dc.subjecttransfer learning
dc.subjectdeep learning
dc.titleA Study on the Impact of Transfer Learning for Deception Detection
dc.type.dcmitext
dc.type.genreThesis
dcterms.accessRightsThe full text of this item is not available at this time because the student has placed this item under an embargo for a period of time. The Libraries are not authorized to provide a copy of this work during the embargo period.
local.embargo.lift2025-08-01
local.embargo.terms2025-08-01
thesis.degree.collegeCollege of Natural Sciences and Mathematics
thesis.degree.departmentComputer Science, Department of
thesis.degree.disciplineComputer Science
thesis.degree.grantorUniversity of Houston
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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