COVID-Fact: Fact Extraction and Verification of Real-World Claims on COVID-19 Pandemic

TL;DR

COVID-Fact is a new dataset and task framework for automatic fact extraction and verification related to COVID-19, aiming to improve misinformation detection with minimal human annotation.

Contribution

The paper introduces COVID-Fact, a large dataset with automated claim verification methods, and formalizes the evidence identification and claim verification tasks for COVID-19 misinformation.

Findings

COVID-Fact contains 4,086 claims with evidence and refutations.

Automated methods reduce the need for human annotation in dataset creation.

The dataset provides a challenging benchmark for future misinformation detection systems.

Abstract

We introduce a FEVER-like dataset COVID-Fact of $4,086$ claims concerning the COVID-19 pandemic. The dataset contains claims, evidence for the claims, and contradictory claims refuted by the evidence. Unlike previous approaches, we automatically detect true claims and their source articles and then generate counter-claims using automatic methods rather than employing human annotators. Along with our constructed resource, we formally present the task of identifying relevant evidence for the claims and verifying whether the evidence refutes or supports a given claim. In addition to scientific claims, our data contains simplified general claims from media sources, making it better suited for detecting general misinformation regarding COVID-19. Our experiments indicate that COVID-Fact will provide a challenging testbed for the development of new systems and our approach will reduce the costs of building domain-specific datasets for detecting misinformation.