{"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/fake-news-detection-via-nlp-is-vulnerable-to","title":"Fake News Detection via NLP is Vulnerable to Adversarial Attacks","arxiv_id":"1901.09657","date":"2019-01-05","proceeding":null,"authors":["Zhixuan Zhou","Huankang Guan","Meghana Moorthy Bhat","Justin Hsu"],"abstract":"News plays a significant role in shaping people's beliefs and opinions. Fake\nnews has always been a problem, which wasn't exposed to the mass public until\nthe past election cycle for the 45th President of the United States. While\nquite a few detection methods have been proposed to combat fake news since\n2015, they focus mainly on linguistic aspects of an article without any fact\nchecking. In this paper, we argue that these models have the potential to\nmisclassify fact-tampering fake news as well as under-written real news.\nThrough experiments on Fakebox, a state-of-the-art fake news detector, we show\nthat fact tampering attacks can be effective. To address these weaknesses, we\nargue that fact checking should be adopted in conjunction with linguistic\ncharacteristics analysis, so as to truly separate fake news from real news. A\ncrowdsourced knowledge graph is proposed as a straw man solution to collecting\ntimely facts about news events.","url_abs":"http://arxiv.org/abs/1901.09657v1","url_pdf":"http://arxiv.org/pdf/1901.09657v1.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":"fake-news-detection-via-nlp-is-vulnerable-to","repo_url":"https://github.com/kyriezoe/Fake_News_Detection","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"fact-checking","task_name":"Fact Checking"},{"task_slug":"fake-news-detection","task_name":"Fake News Detection"}],"methods":[{"method_slug":"accuracy-robustness-area-ara","method_name":"Accuracy-Robustness Area (ARA)"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1901.09657","atlas_url":"https://app.syntology.ai/?focus=1901.09657","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}