TITLE: Fighting an Infodemic: COVID-19 Fake News Dataset 

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
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
Fighting an Infodemic: COVID-19 Fake News Dataset
http://arxiv.org/abs/2011.03327v4