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Fake news has altered society in negative ways in politics and culture. Ithas adversely affected both online social network systems as well as offlinecommunities and conversations. Using automatic machine learning classificationmodels is an efficient way to combat the widespread dissemination of fake news.However, a lack of effective, comprehensive datasets has been a problem forfake news research and detection model development. Prior fake news datasets donot provide multimodal text and image data, metadata, comment data, andfine-grained fake news categorization at the scale and breadth of our dataset.We present Fakeddit, a novel multimodal dataset consisting of over 1 millionsamples from multiple categories of fake news. After being processed throughseveral stages of review, the samples are labeled according to 2-way, 3-way,and 6-way classification categories through distant supervision. We constructhybrid text+image models and perform extensive experiments for multiplevariations of classification, demonstrating the importance of the novel aspectof multimodality and fine-grained classification unique to Fakeddit.

r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection