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

Published at CONSTRAINT-2021, Collocated with AAAI-2021

Cited by in corpus (5)

• Evaluating Deep Learning Approaches for Covid19 Fake News Detection
• g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection
• A Survey of COVID-19 Misinformation: Datasets, Detection Techniques and Open Issues
• Identification of COVID-19 related Fake News via Neural Stacking
• A Comparative Study on COVID-19 Fake News Detection Using Different Transformer Based Models