Author: Patwa, Parth; Sharma, Shivam; Pykl, Srinivas; Guptha, Vineeth; Kumari, Gitanjali; Akhtar, Md Shad; Ekbal, Asif; Das, Amitava; Chakraborty, Tanmoy
Title: Fighting an Infodemic: COVID-19 Fake News Dataset Cord-id: ncwwx77r Document date: 2020_11_6
ID: ncwwx77r
Snippet: 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
Document: 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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