Fighting an Infodemic: COVID-19 Fake News Dataset
Computation and Language
2021-05-27 v4 Information Retrieval
Social and Information Networks
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
Keywords
Cite
@article{arxiv.2011.03327,
title = {Fighting an Infodemic: COVID-19 Fake News Dataset},
author = {Parth Patwa and Shivam Sharma and Srinivas Pykl and Vineeth Guptha and Gitanjali Kumari and Md Shad Akhtar and Asif Ekbal and Amitava Das and Tanmoy Chakraborty},
journal= {arXiv preprint arXiv:2011.03327},
year = {2021}
}
Comments
Published at CONSTRAINT-2021, Collocated with AAAI-2021