Publication detail

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

SRBA, I. PECHER, B. TOMLEIN, M. MÓRO, R. ŠTEFANCOVÁ, E. ŠIMKO, J. BIELIKOVÁ, M.

Original Title

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

Type

conference paper

Language

English

Original 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.

Keywords

medical misinformation, dataset, fact-checking, Monant platform

Authors

SRBA, I.; PECHER, B.; TOMLEIN, M.; MÓRO, R.; ŠTEFANCOVÁ, E.; ŠIMKO, J.; BIELIKOVÁ, M.

Released

8. 7. 2022

Publisher

Association for Computing Machinery

Location

Madrid

ISBN

978-1-4503-8732-3

Book

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages from

2949

Pages to

2959

Pages count

11

URL

https://dl.acm.org/doi/10.1145/3477495.3531726

BibTex

@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"
}