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Fighting an Infodemic: COVID-19 Fake News Dataset

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arxiv 2011.03327 v4 pith:HRFIGGXE submitted 2020-11-06 cs.CL cs.IRcs.SI

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
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Tanmoy Chakraborty
This is my paper
classification cs.CL cs.IRcs.SI
keywords covid-19datasetfakenewsannotatedfightinginfodemicmachine
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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