Fighting an Infodemic: COVID-19 Fake News Dataset

TL;DR

This paper introduces a manually annotated dataset of 10,700 COVID-19 related social media posts and articles, and benchmarks machine learning models for fake news detection, achieving up to 93.46% F1-score with SVM.

Contribution

It provides a new, publicly available dataset for COVID-19 fake news detection and evaluates baseline machine learning models on this dataset.

Findings

SVM achieved 93.46% F1-score

Dataset includes 10,700 annotated posts and articles

Benchmark results establish baselines for future research

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