DOI: 10.36190/2021.39

Published: 2021-06-01
Evidence based Automatic Fact-Checking for Climate Change Misinformation
Gengyu Wang, Lawrence Chillrud, Kathleen McKeown

Misinformation surrounding climate change (CC) proliferates across the internet at such rapid speeds and in such large quantities that human fact-checkers are unable to feasibly verify the veracity of most online CC-related information. While automatic fact-checking algorithms can supplement human factchecking efforts, existing models suffer from a lack of domainspecific training data to robustly fact-check CC information. To address this problem, we tailor an existing automatic factchecking system to the CC domain by introducing the popular semi-supervised training method, Unsupervised Data Augmentation (UDA), into our system's pipeline, allowing us to leverage large amounts of unlabeled CC-related claims. We evaluate our fact-checking model on the CC fact-checking dataset CLIMATE-FEVER, yielding a state-of-the-art (SotA) F1 score of 0.7182, improving upon the previously reported SotA F1 score of 0.3285.