{"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/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","arxiv_id":"1707.03264","date":"2017-07-11","proceeding":null,"authors":["Benjamin Riedel","Isabelle Augenstein","Georgios P. Spithourakis","Sebastian Riedel"],"abstract":"Identifying public misinformation is a complicated and challenging task. An\nimportant part of checking the veracity of a specific claim is to evaluate the\nstance different news sources take towards the assertion. Automatic stance\nevaluation, i.e. stance detection, would arguably facilitate the process of\nfact checking. In this paper, we present our stance detection system which\nclaimed third place in Stage 1 of the Fake News Challenge. Despite our\nstraightforward approach, our system performs at a competitive level with the\ncomplex ensembles of the top two winning teams. We therefore propose our system\nas the 'simple but tough-to-beat baseline' for the Fake News Challenge stance\ndetection task.","url_abs":"http://arxiv.org/abs/1707.03264v2","url_pdf":"http://arxiv.org/pdf/1707.03264v2.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":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/uclmr/fakenewschallenge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/ankitp544/stance_detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/chimera-detector/Extension","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/chimera-detector/Server","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/chimera-detector/experienceExtension","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/gabrielsaruhashi/anti-fake","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/harshita97/hoaxbait","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/pmallari/AmazonSentimentAnalysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"a-simple-but-tough-to-beat-baseline-for-the","repo_url":"https://github.com/uclnlp/fakenewschallenge","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"misinformation","task_name":"Misinformation"},{"task_slug":"stance-detection","task_name":"Stance Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/fake-news-detection-on-fnc-1","task":"Fake News Detection","dataset":"FNC-1","model":"3rd place at FNC-1 - Team UCL Machine Reading (Riedel et al., 2017)","rank_in_archive_order":5,"of":10,"metrics":{"Per-class Accuracy (Agree)":"44.04","Per-class Accuracy (Disagree)":"6.60","Per-class Accuracy (Discuss)":"81.38","Per-class Accuracy (Unrelated)":"97.90","Weighted Accuracy":"81.72"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/1707.03264","atlas_url":"https://app.syntology.ai/?focus=1707.03264","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}