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

arXiv: https://arxiv.org/abs/2204.12294 · HTML: https://mlsanity.com/paper/2204.12294/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 53 references that are in the map.
- First posted on arXiv 26 Apr 2022.

## Builds on (most cited first, 25 of 25)

- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [A Survey on Automated Fact-Checking](https://mlsanity.com/paper/2108.11896/) (2021)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [FakeNewsNet: A Data Repository with News Content, Social Context and Spatialtemporal Information for Studying Fake News on Social Media](https://mlsanity.com/paper/1809.01286/) (2018)
- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [A Survey on Stance Detection for Mis- and Disinformation Identification](https://mlsanity.com/paper/2103.00242/) (2021)
- [Reasoning Over Semantic-Level Graph for Fact Checking](https://mlsanity.com/paper/1909.03745/) (2019)
- [FakeCovid -- A Multilingual Cross-domain Fact Check News Dataset for COVID-19](https://mlsanity.com/paper/2006.11343/) (2020)
- [This Just In: Fake News Packs a Lot in Title, Uses Simpler, Repetitive Content in Text Body, More Similar to Satire than Real News](https://mlsanity.com/paper/1703.09398/) (2017)
- [Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2109.11427/) (2021)
- [A Retrospective Analysis of the Fake News Challenge Stance Detection Task](https://mlsanity.com/paper/1806.05180/) (2018)
- [Integrating Stance Detection and Fact Checking in a Unified Corpus](https://mlsanity.com/paper/1804.08012/) (2018)
- [BanFakeNews: A Dataset for Detecting Fake News in Bangla](https://mlsanity.com/paper/2004.08789/) (2020)
- [Combating Fake News: A Survey on Identification and Mitigation Techniques](https://mlsanity.com/paper/1901.06437/) (2019)
- [Weak Supervision for Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/1912.12520/) (2019)
- [A simple but tough-to-beat baseline for the Fake News Challenge stance detection task](https://mlsanity.com/paper/1707.03264/) (2017)
- [Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository](https://mlsanity.com/paper/2002.00837/) (2020)
- [Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document](https://mlsanity.com/paper/2109.07410/) (2021)
- [An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes](https://mlsanity.com/paper/2203.13769/) (2022)
- [Combining Similarity Features and Deep Representation Learning for Stance Detection in the Context of Checking Fake News](https://mlsanity.com/paper/1811.00706/) (2018)
- [In Search of Credible News](https://mlsanity.com/paper/1911.08125/) (2019)

## Built on by (most cited first, 10 of 10)

- [Generative Large Language Models in Automated Fact-Checking: A Survey](https://mlsanity.com/paper/2407.02351/) (2024)
- [Evaluating Large Language Models for Health-related Queries with Presuppositions](https://mlsanity.com/paper/2312.08800/) (2023)
- [SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discourse](https://mlsanity.com/paper/2206.07360/) (2022)
- [One Persona, Many Cues, Different Results: How Sociodemographic Cues Impact LLM Personalization](https://mlsanity.com/paper/2601.18572/) (2026)
- [Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems](https://mlsanity.com/paper/2211.12143/) (2022)
- [A Scoping Review of Natural Language Processing in Addressing Medically Inaccurate Information: Errors, Misinformation, and Hallucination](https://mlsanity.com/paper/2505.00008/) (2025)
- [Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts](https://mlsanity.com/paper/2309.09877/) (2023)
- [FarExStance: Explainable Stance Detection for Farsi](https://mlsanity.com/paper/2412.14008/) (2024)
- [From Generation to Detection: A Multimodal Multi-Task Dataset for Benchmarking Health Misinformation](https://mlsanity.com/paper/2505.18685/) (2025)
- [Integrating Persuasion Theory into the Epidemiological Modelling of Health Misinformation Spread on Social Media](https://mlsanity.com/paper/2608.15689/) (2026)

## Abstract (arXiv)

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.