{"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-retrospective-analysis-of-the-fake-news-1","title":"A Retrospective Analysis of the Fake News Challenge Stance Detection Task","arxiv_id":"1806.05180","date":"2018-06-13","proceeding":null,"authors":["Andreas Hanselowski","Avinesh PVS","Benjamin Schiller","Felix Caspelherr","Debanjan Chaudhuri","Christian M. Meyer","Iryna Gurevych"],"abstract":"The 2017 Fake News Challenge Stage 1 (FNC-1) shared task addressed a stance\nclassification task as a crucial first step towards detecting fake news. To\ndate, there is no in-depth analysis paper to critically discuss FNC-1's\nexperimental setup, reproduce the results, and draw conclusions for\nnext-generation stance classification methods. In this paper, we provide such\nan in-depth analysis for the three top-performing systems. We first find that\nFNC-1's proposed evaluation metric favors the majority class, which can be\neasily classified, and thus overestimates the true discriminative power of the\nmethods. Therefore, we propose a new F1-based metric yielding a changed system\nranking. Next, we compare the features and architectures used, which leads to a\nnovel feature-rich stacked LSTM model that performs on par with the best\nsystems, but is superior in predicting minority classes. To understand the\nmethods' ability to generalize, we derive a new dataset and perform both\nin-domain and cross-domain experiments. Our qualitative and quantitative study\nhelps interpreting the original FNC-1 scores and understand which features help\nimproving performance and why. Our new dataset and all source code used during\nthe reproduction study are publicly available for future research.","url_abs":"http://arxiv.org/abs/1806.05180v1","url_pdf":"http://arxiv.org/pdf/1806.05180v1.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-retrospective-analysis-of-the-fake-news-1","repo_url":"https://github.com/UKPLab/coling2018-fake-news-challenge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-retrospective-analysis-of-the-fake-news-1","repo_url":"https://github.com/UKPLab/coling2018_fake-news-challenge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-retrospective-analysis-of-the-fake-news-1","repo_url":"https://github.com/hanselowski/athene_system","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-retrospective-analysis-of-the-fake-news-1","repo_url":"https://github.com/chiahuiliu/FNC_Python3","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"a-retrospective-analysis-of-the-fake-news-1","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-retrospective-analysis-of-the-fake-news-1","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-retrospective-analysis-of-the-fake-news-1","repo_url":"https://github.com/chimera-detector/experienceExtension","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"stance-classification","task_name":"Stance Classification"},{"task_slug":"stance-detection","task_name":"Stance Detection"}],"methods":[{"method_slug":"lstm","method_name":"LSTM"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1806.05180","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}