{"id":{"repo_id":"york","oai_identifier":"oai:yorkspace.library.yorku.ca:10315/43660"},"canonical_url":"https://search.dev.ndltd.org/etd/york/oai:yorkspace.library.yorku.ca:10315/43660","repository":{"repo_id":"york","name":"York University","base_url":"https://yorkspace.library.yorku.ca/oai/request"},"display":{"title":"AI-Driven Fake News Detection: Trends, Techniques, and Experimental Analysis","abstract":"The rapid spread of fake news poses a significant challenge to information accuracy. This thesis highlights fake news definitions and characteristics, introducing a taxonomy that categorizes AI-driven detection methods into model-centric and process-centric approaches. We evaluate various approaches ranging from traditional machine learning to trending AI methodologies, focusing on techniques like data augmentation, information extraction, and results explanation. To re-evaluate classical algorithms, this work provides a detailed analysis of a 2016 U.S. election dataset. 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