FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19

TL;DR

FakeCovid is a multilingual, cross-domain dataset of over 5,000 COVID-19 news articles fact-checked across 40 languages, enabling improved automatic fake news detection.

Contribution

This paper introduces the first large-scale multilingual COVID-19 fact-checking dataset with manual annotations across multiple categories and demonstrates a classifier achieving 0.76 F1 score.

Findings

Classifier achieves 0.76 F1 score in detecting fake news.

Dataset covers 40 languages and 105 countries.

Provides a valuable resource for multilingual fake news detection.

Abstract

In this paper, we present a first multilingual cross-domain dataset of 5182 fact-checked news articles for COVID-19, collected from 04/01/2020 to 15/05/2020. We have collected the fact-checked articles from 92 different fact-checking websites after obtaining references from Poynter and Snopes. We have manually annotated articles into 11 different categories of the fact-checked news according to their content. The dataset is in 40 languages from 105 countries. We have built a classifier to detect fake news and present results for the automatic fake news detection and its class. Our model achieves an F1 score of 0.76 to detect the false class and other fact check articles. The FakeCovid dataset is available at Github.