{"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/declare-debunking-fake-news-and-false-claims","title":"DeClarE: Debunking Fake News and False Claims using Evidence-Aware Deep Learning","arxiv_id":"1809.06416","date":"2018-09-17","proceeding":"EMNLP 2018 10","authors":["Kashyap Popat","Subhabrata Mukherjee","Andrew Yates","Gerhard Weikum"],"abstract":"Misinformation such as fake news is one of the big challenges of our society.\nResearch on automated fact-checking has proposed methods based on supervised\nlearning, but these approaches do not consider external evidence apart from\nlabeled training instances. Recent approaches counter this deficit by\nconsidering external sources related to a claim. However, these methods require\nsubstantial feature modeling and rich lexicons. This paper overcomes these\nlimitations of prior work with an end-to-end model for evidence-aware\ncredibility assessment of arbitrary textual claims, without any human\nintervention. It presents a neural network model that judiciously aggregates\nsignals from external evidence articles, the language of these articles and the\ntrustworthiness of their sources. It also derives informative features for\ngenerating user-comprehensible explanations that makes the neural network\npredictions transparent to the end-user. Experiments with four datasets and\nablation studies show the strength of our method.","url_abs":"http://arxiv.org/abs/1809.06416v1","url_pdf":"http://arxiv.org/pdf/1809.06416v1.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":"declare-debunking-fake-news-and-false-claims","repo_url":"https://github.com/Kanaderu/nlp_credibility","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"declare-debunking-fake-news-and-false-claims","repo_url":"https://github.com/atulkumarin/DeClare","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"misinformation","task_name":"Misinformation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.06416","atlas_url":"https://app.syntology.ai/?focus=1809.06416","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}