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Fighting an Infodemic: COVID-19 Fake News Dataset
- Publication Year :
- 2020
-
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<br />Comment: Published at CONSTRAINT-2021, Collocated with AAAI-2021
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2011.03327
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1007/978-3-030-73696-5_3