{"url":"/dataset/rawfc","name":"RAWFC","full_name":"RAWFC","description_markdown":"For RAWFC, we constructed it from scratch by collecting the claims from Snopes and relevant raw reports by retrieving claim keywords. To alleviate the dependency of fact-checked reports, RAWFC was constructed by using raw reports (from scratch), where gold labels refer to Snopes. Each instance in the train/val/test set is presented as a signle file. \r\n\r\nProvide:\r\n\r\n* A novel benchmark dataset for explainable fake news detection","description_withheld":null,"homepage":"https://github.com/Nicozwy/CofCED/tree/main/Datasets/RAWFC","introduced_date":"2022-09-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-coarse-to-fine-cascaded-evidence","title":"A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection","first_author":"Zhiwei Yang","url":null},"license":{"name":"Apache-2.0 license","url":null},"modalities":[],"tasks":[{"name":"Stance Detection","url":"/task/stance-detection","datasets_with_task":"/datasets/task/stance-detection"},{"name":"Fake News Detection","url":"/task/fake-news-detection","datasets_with_task":"/datasets/task/fake-news-detection"},{"name":"Misinformation","url":"/task/misinformation","datasets_with_task":"/datasets/task/misinformation"}],"languages":[],"variants":["LIAR"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/fake-news-detection-on-rawfc","task":"Fake News Detection","dataset_variant":"RAWFC","rows":6,"metrics":["F1"],"first_row_in_archive_order":{"model":"Persuasive Writing Strategy","paper":"/paper/misinformation-detection-using-persuasive","metrics":{"F1":"55.8"},"code_links":[{"title":"hlr/misinformation-detection","url":"https://github.com/hlr/misinformation-detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/towards-llm-based-fact-verification-on-news","title":"Towards LLM-based Fact Verification on News Claims with a Hierarchical Step-by-Step Prompting Method","date":"2023-09-30","rows_on_this_dataset":4,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/misinformation-detection-using-persuasive","title":"Using Persuasive Writing Strategies to Explain and Detect Health Misinformation","date":"2022-11-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-coarse-to-fine-cascaded-evidence","title":"A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection","date":"2022-09-29","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"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."}