{"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/where-is-your-evidence-improving-fact","title":"Where is Your Evidence: Improving Fact-checking by Justification Modeling","arxiv_id":null,"date":"2018-11-01","proceeding":"WS 2018 11","authors":["Tariq Alhindi","Savvas Petridis","Smar Muresan","a"],"abstract":"Fact-checking is a journalistic practice that compares a claim made publicly against trusted sources of facts. Wang (2017) introduced a large dataset of validated claims from the POLITIFACT.com website (LIAR dataset), enabling the development of machine learning approaches for fact-checking. However, approaches based on this dataset have focused primarily on modeling the claim and speaker-related metadata, without considering the evidence used by humans in labeling the claims. We extend the LIAR dataset by automatically extracting the justification from the fact-checking article used by humans to label a given claim. We show that modeling the extracted justification in conjunction with the claim (and metadata) provides a significant improvement regardless of the machine learning model used (feature-based or deep learning) both in a binary classification task (true, false) and in a six-way classification task (pants on fire, false, mostly false, half true, mostly true, true).","url_abs":"https://aclanthology.org/W18-5513","url_pdf":"https://aclanthology.org/W18-5513.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":"where-is-your-evidence-improving-fact","repo_url":"https://github.com/Tariq60/LIAR-PLUS","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null},{"paper_slug":"where-is-your-evidence-improving-fact","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"}}],"tasks":[{"task_slug":"argument-mining","task_name":"Argument Mining"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"emotion-recognition","task_name":"Emotion Recognition"},{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}