Ensemble Learning of Deep Bidirectional Transformers \\for Emotion and Psychology analysis of textual \\Data for Social Media Bot detection

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dc.contributor.advisorJones, Jim
dc.contributor.advisorUzuner, Ozlem
dc.creatorHeidari, Maryam
dc.date.accessioned2023-03-17T19:05:33Z
dc.date.available2023-03-17T19:05:33Z
dc.date.issued2022
dc.description.abstractSocial media platforms can expose influential trends in many aspects of everyday life. However, the trends they represent can represent disinformation. Social bots are one of the significant sources of disinformation in social media. Thus, social bots can pose severe threats to society and public opinion. This research aims to develop machine learning and NLP models to detect bots. In recent years, the growth of social media dictates more attention to contextual content of online comments compared to the bot detection approaches in the past. Advanced natural language processing models and transfer learning is a new approach for social media bot detection in this research.
dc.format.extent160 pages
dc.format.mediumdoctoral dissertations
dc.identifier.urihttps://hdl.handle.net/1920/13145
dc.language.isoen
dc.rightsCopyright 2022 Maryam Heidari
dc.rights.urihttps://rightsstatements.org/vocab/InC/1.0
dc.subjectDeep Learning
dc.subjectMachine learning
dc.subjectNatural Language Processing
dc.subjectSocial Media
dc.subject.keywordsComputer science
dc.titleEnsemble Learning of Deep Bidirectional Transformers \\for Emotion and Psychology analysis of textual \\Data for Social Media Bot detection
dc.typeText
thesis.degree.disciplineInformation Technology
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral
thesis.degree.namePh.D. in Information Technology

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