Fighting an Infodemic: COVID-19 Fake News Dataset

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 perform a binary classification task (real vs fake) and 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 93.32% F1-score with SVM on the test set. The data and code is available at: https://github.com/parthpatwa/covid19-fake-news-dectection .