{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/liar-liar-pants-on-fire-a-new-benchmark","title":"\"Liar, Liar Pants on Fire\": A New Benchmark Dataset for Fake News Detection","arxiv_id":"1705.00648","date":"2017-05-01","proceeding":"ACL 2017 7","authors":["William Yang Wang"],"abstract":"Automatic fake news detection is a challenging problem in deception\ndetection, and it has tremendous real-world political and social impacts.\nHowever, statistical approaches to combating fake news has been dramatically\nlimited by the lack of labeled benchmark datasets. In this paper, we present\nliar: a new, publicly available dataset for fake news detection. We collected a\ndecade-long, 12.8K manually labeled short statements in various contexts from\nPolitiFact.com, which provides detailed analysis report and links to source\ndocuments for each case. This dataset can be used for fact-checking research as\nwell. Notably, this new dataset is an order of magnitude larger than previously\nlargest public fake news datasets of similar type. Empirically, we investigate\nautomatic fake news detection based on surface-level linguistic patterns. We\nhave designed a novel, hybrid convolutional neural network to integrate\nmeta-data with text. We show that this hybrid approach can improve a text-only\ndeep learning model.","url_abs":"http://arxiv.org/abs/1705.00648v1","url_pdf":"http://arxiv.org/pdf/1705.00648v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/ExploringLies/lies-have-short-legs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/JelenaBanjac/AppliedDataAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/JelenaBanjac/lies-have-short-legs","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/SindhuMadi/FakeNewsDetection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/bedarkarpriyanka/NLP-Project-Fake-News-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/kiranrawat/Detecting-Fake-News-On-Social-Media","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/mansoor9743/Fake-News-Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/mikanikos/ADA_Project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/siddarthhari95/NLP-Fake-News_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/ekagra-ranjan/fake-news-detection-LIAR-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"liar-liar-pants-on-fire-a-new-benchmark","repo_url":"https://github.com/manideep2510/siamese-BERT-fake-news-detection-LIAR","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"deception-detection","task_name":"Deception Detection"},{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"fake-news-detection","task_name":"Fake News Detection"}],"methods":[],"datasets_introduced":[{"slug":"liar","name":"LIAR","full_name":"LIAR"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/fake-news-detection-on-liar","task":"Fake News Detection","dataset":"LIAR","model":"Hybrid CNNs (Text + All)","rank_in_archive_order":1,"of":4,"metrics":{"Test Accuracy":"0.274","Validation Accuracy":"0.247"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-liar","task":"Fake News Detection","dataset":"LIAR","model":"CNNs","rank_in_archive_order":2,"of":4,"metrics":{"Test Accuracy":"0.27","Validation Accuracy":"0.26"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-liar","task":"Fake News Detection","dataset":"LIAR","model":"Hybrid CNNs (Text + Speaker)","rank_in_archive_order":3,"of":4,"metrics":{"Test Accuracy":"0.248","Validation Accuracy":"0.277"},"uses_additional_data":false},{"leaderboard":"/sota/fake-news-detection-on-liar","task":"Fake News Detection","dataset":"LIAR","model":"Bi-LSTMs","rank_in_archive_order":4,"of":4,"metrics":{"Test Accuracy":"0.233","Validation Accuracy":"0.223"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.00648","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}