Publication Details
Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims
Pecher Branislav, Ing. (DCGM)
TOMLEIN, M.
MÓRO, R.
ŠTEFANCOVÁ, E.
Šimko Jakub, doc. Ing., PhD. (DCGM)
Bieliková Mária, prof. Ing., Ph.D. (DCGM)
medical misinformation, dataset, fact-checking, Monant platform
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.
@inproceedings{BUT180392,
author="SRBA, I. and PECHER, B. and TOMLEIN, M. and MÓRO, R. and ŠTEFANCOVÁ, E. and ŠIMKO, J. and BIELIKOVÁ, M.",
title="Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims",
booktitle="Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval",
year="2022",
pages="2949--2959",
publisher="Association for Computing Machinery",
address="Madrid",
doi="10.1145/3477495.3531726",
isbn="978-1-4503-8732-3",
url="https://dl.acm.org/doi/10.1145/3477495.3531726"
}