{"url":"/dataset/fakeddit","name":"Fakeddit","full_name":null,"description_markdown":"**Fakeddit** is a novel multimodal dataset for fake news detection consisting of over 1 million samples from multiple categories of fake news. After being processed through several stages of review, the samples are labeled according to 2-way, 3-way, and 6-way classification categories through distant supervision.\r\n\r\nSource: [https://fakeddit.netlify.app/](https://fakeddit.netlify.app/)","description_withheld":null,"homepage":"https://github.com/entitize/fakeddit","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/rfakeddit-a-new-multimodal-benchmark-dataset","title":"r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection","first_author":"Kai Nakamura","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Texts","url":"/datasets/modality/texts"}],"tasks":[{"name":"Fake News Detection","url":"/task/fake-news-detection","datasets_with_task":"/datasets/task/fake-news-detection"}],"languages":[],"variants":["Fakeddit"],"data_loaders":[{"repo":"https://github.com/entitize/fakeddit","url":"https://github.com/entitize/fakeddit","frameworks":[]}],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}