{"url":"/dataset/fnc-1","name":"FNC-1","full_name":"Fake News Challenge Stage 1","description_markdown":"**FNC-1** was designed as a stance detection dataset and it contains 75,385 labeled headline and article pairs. The pairs are labelled as either agree, disagree, discuss, and unrelated. Each headline in the dataset is phrased as a statement\r\n\r\nSource: [Investigating Rumor News Using Agreement-Aware Search](https://arxiv.org/abs/1802.07398)\nImage Source: [http://www.fakenewschallenge.org/](http://www.fakenewschallenge.org/)","description_withheld":null,"homepage":"http://www.fakenewschallenge.org/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"The fake news challenge: Exploring how artificial intelligence technologies could be leveraged to combat fake news","first_author":null,"url":"http://www.fakenewschallenge.org/"},"license":null,"modalities":[{"name":"Texts","url":"/datasets/modality/texts"}],"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"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["FNC-1"],"data_loaders":[],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/fake-news-detection-on-fnc-1","task":"Fake News Detection","dataset_variant":"FNC-1","rows":10,"metrics":["Weighted Accuracy","Per-class Accuracy (Unrelated)","Per-class Accuracy (Agree)","Per-class Accuracy (Disagree)","Per-class Accuracy (Discuss)"],"first_row_in_archive_order":{"model":"Sepúlveda-Torres R., Vicente M., Saquete E., Lloret E., Palomar M. (2021)","paper":"/paper/exploring-summarization-to-enhance-headline","metrics":{"Per-class Accuracy (Agree)":"75.03","Per-class Accuracy (Disagree)":"63.41","Per-class Accuracy (Discuss)":"85.97","Per-class Accuracy (Unrelated)":"99.36","Weighted Accuracy":"90.73"},"code_links":[{"title":"rsepulveda911112/Headline-Stance-Detection","url":"https://github.com/rsepulveda911112/Headline-Stance-Detection"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/stance-detection-on-fnc-1","task":"Stance Detection","dataset_variant":"FNC-1","rows":2,"metrics":["F1"],"first_row_in_archive_order":{"model":"TESTED","paper":"/paper/topic-guided-sampling-for-data-efficient","metrics":{"F1":"83.17"},"code_links":[{"title":"copenlu/TESTED","url":"https://github.com/copenlu/TESTED"},{"title":"copenlu/TESTED","url":"https://github.com/copenlu/TESTED/blob/main/README.md"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/explainable-deep-learning-a-visual-analytics","title":"Explainable Deep Learning: A Visual Analytics Approach with Transition Matrices","date":"2024-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/topic-guided-sampling-for-data-efficient","title":"Topic-Guided Sampling For Data-Efficient Multi-Domain Stance Detection","date":"2023-06-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/combination-of-convolution-neural-networks-1","title":"Combination Of Convolution Neural Networks And Deep Neural Networks For Fake News Detection","date":"2022-10-15","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/exploring-summarization-to-enhance-headline","title":"Exploring Summarization to Enhance Headline Stance Detection","date":"2021-06-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/combining-similarity-features-and-deep","title":"Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News","date":"2018-11-02","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/automatic-stance-detection-using-end-to-end","title":"Automatic Stance Detection Using End-to-End Memory Networks","date":"2018-04-20","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/on-the-benefit-of-combining-neural","title":"On the Benefit of Combining Neural, Statistical and External Features for Fake News Identification","date":"2017-12-11","rows_on_this_dataset":4,"code_links":1,"syntology":null},{"paper":"/paper/a-simple-but-tough-to-beat-baseline-for-the","title":"A simple but tough-to-beat baseline for the Fake News Challenge stance detection task","date":"2017-07-11","rows_on_this_dataset":1,"code_links":9,"syntology":null}],"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."}