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arXiv · 2011.03327

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
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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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Parth Patwa, Shivam Sharma, Srinivas Pykl, Vineeth Guptha, Gitanjali Kumari, Md Shad Akhtar, Asif Ekbal, Amitava Das, Tanmoy Chakraborty. 2021-05-26. Fighting an Infodemic: COVID-19 Fake News Dataset. https://doi.org/10.1007/978-3-030-73696-5_3

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