TITLE: Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims 

ABSTRACT: False information has a significant negative influence on individuals as well
as on the whole society. Especially in the current COVID-19 era, we witness an
unprecedented growth of medical misinformation. To help tackle this problem
with machine learning approaches, we are publishing a feature-rich dataset of
approx. 317k medical news articles/blogs and 3.5k fact-checked claims. It also
contains 573 manually and more than 51k automatically labelled mappings between
claims and articles. Mappings consist of claim presence, i.e., whether a claim
is contained in a given article, and article stance towards the claim. We
provide several baselines for these two tasks and evaluate them on the manually
labelled part of the dataset. The dataset enables a number of additional tasks
related to medical misinformation, such as misinformation characterisation
studies or studies of misinformation diffusion between sources.
False information has a significant negative influence on individuals as well
as on the whole society. Especially in the current COVID-19 era, we witness an
unprecedented growth of medical misinformation. To help tackle this problem
with machine learning approaches, we are publishing a feature-rich dataset of
approx. 317k medical news articles/blogs and 3.5k fact-checked claims. It also
contains 573 manually and more than 51k automatically labelled mappings between
claims and articles. Mappings consist of claim presence, i.e., whether a claim
is contained in a given article, and article stance towards the claim. We
provide several baselines for these two tasks and evaluate them on the manually
labelled part of the dataset. The dataset enables a number of additional tasks
related to medical misinformation, such as misinformation characterisation
studies or studies of misinformation diffusion between sources.
Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims
http://arxiv.org/abs/2204.12294v1