{"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/fighting-an-infodemic-covid-19-fake-news","title":"Fighting an Infodemic: COVID-19 Fake News Dataset","arxiv_id":"2011.03327","date":"2020-11-06","proceeding":null,"authors":["Parth Patwa","Shivam Sharma","Srinivas PYKL","Vineeth Guptha","Gitanjali Kumari","Md Shad Akhtar","Asif Ekbal","Amitava Das","Tanmoy Chakraborty"],"abstract":"Along with COVID-19 pandemic we are also fighting an `infodemic'. Fake news and rumors are rampant on social media. Believing in rumors can cause significant harm. This is further exacerbated at the time of a pandemic. To tackle this, we curate and release a manually annotated dataset of 10,700 social media posts and articles of real and fake news on COVID-19. We benchmark the annotated dataset with four machine learning baselines - Decision Tree, Logistic Regression, Gradient Boost, and Support Vector Machine (SVM). We obtain the best performance of 93.46% F1-score with SVM. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection","url_abs":"https://arxiv.org/abs/2011.03327v4","url_pdf":"https://arxiv.org/pdf/2011.03327v4.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":"fighting-an-infodemic-covid-19-fake-news","repo_url":"https://github.com/parthpatwa/covid19-fake-news-detection","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fighting-an-infodemic-covid-19-fake-news","repo_url":"https://github.com/diptamath/covid_fake_news","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"logistic-regression","method_name":"Logistic Regression"},{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[{"slug":"covid-19-fake-news-dataset","name":"COVID-19 Fake News Dataset","full_name":"COVID19 Fake News Detection in English"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2011.03327","atlas_url":"https://app.syntology.ai/?focus=2011.03327","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}