Fighting an Infodemic: COVID-19 Fake News Dataset

• URL: http://arxiv.org/abs/2011.03327v4
• Date: Wed, 26 May 2021 15:38:55 GMT
• Title: Fighting an Infodemic: COVID-19 Fake News Dataset
• Authors: Parth Patwa, Shivam Sharma, Srinivas Pykl, Vineeth Guptha, Gitanjali Kumari, Md Shad Akhtar, Asif Ekbal, Amitava Das, Tanmoy Chakraborty
• Abstract summary: Fake news and rumors are rampant on social media. 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.
• Score: 40.418407303807456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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