CLIMATE-FEVER: A Dataset for Verification of Real-World Climate Claims

TL;DR

CLIMATE-FEVER is a new dataset designed to help verify climate change claims, aiming to improve AI algorithms' ability to support climate-related fact-checking and combat misinformation.

Contribution

The paper introduces CLIMATE-FEVER, a novel dataset for climate claim verification, adapting FEVER methodology to real-world climate claims with expert input.

Findings

Dataset facilitates research in climate claim verification

Modeling real-world climate claims presents unique challenges

Encourages collaboration between climate science and AI communities

Abstract

We introduce CLIMATE-FEVER, a new publicly available dataset for verification of climate change-related claims. By providing a dataset for the research community, we aim to facilitate and encourage work on improving algorithms for retrieving evidential support for climate-specific claims, addressing the underlying language understanding challenges, and ultimately help alleviate the impact of misinformation on climate change. We adapt the methodology of FEVER [1], the largest dataset of artificially designed claims, to real-life claims collected from the Internet. While during this process, we could rely on the expertise of renowned climate scientists, it turned out to be no easy task. We discuss the surprising, subtle complexity of modeling real-world climate-related claims within the \textsc{fever} framework, which we believe provides a valuable challenge for general natural language understanding. We hope that our work will mark the beginning of a new exciting long-term joint effort by the climate science and AI community.