Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/158374
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dc.contributor.authorChen, Hanzhien_US
dc.date.accessioned2022-05-25T02:25:52Z-
dc.date.available2022-05-25T02:25:52Z-
dc.date.issued2022-
dc.identifier.citationChen, H. (2022). Deep learning-based fake news detection. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158374en_US
dc.identifier.urihttps://hdl.handle.net/10356/158374-
dc.description.abstractIdentifying the truthfulness of news is crucial as it has a great societal impact, and its importance has increased every year since the information age. After the deep learning models were introduced to generate fake news, it become more difficult for a human to identify fake news. Therefore, researchers proposed neural network models to detect fake news but most models only focus on a few datasets. This dissertation evaluates different types of methods on various datasets for overall performance. Furthermore, we discuss the application range of different types of detection methods.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Electrical and electronic engineering::Electronic systems::Signal processingen_US
dc.subjectEngineering::Computer science and engineering::Computing methodologies::Artificial intelligenceen_US
dc.titleDeep learning-based fake news detectionen_US
dc.typeThesis-Master by Courseworken_US
dc.contributor.supervisorAlex Chichung Koten_US
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.description.degreeMaster of Science (Signal Processing)en_US
dc.contributor.researchRapid-Rich Object Search (ROSE) Laben_US
dc.contributor.supervisoremailEACKOT@ntu.edu.sgen_US
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