# Automated Fact Checking in the News Room

arXiv: https://arxiv.org/abs/1904.02037 · HTML: https://mlsanity.com/paper/1904.02037/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 13 of 13)

- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [AVeriTeC: A Dataset for Real-world Claim Verification with Evidence from the Web](https://mlsanity.com/paper/2305.13117/) (2023)
- [The Automated Verification of Textual Claims (AVeriTeC) Shared Task](https://mlsanity.com/paper/2410.23850/) (2024)
- [Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking](https://mlsanity.com/paper/2502.09083/) (2025)
- [Assisting the Human Fact-Checkers: Detecting All Previously Fact-Checked Claims in a Document](https://mlsanity.com/paper/2109.07410/) (2021)
- [Can LLMs Improve Multimodal Fact-Checking by Asking Relevant Questions?](https://mlsanity.com/paper/2410.04616/) (2024)
- [FacTeR-Check: Semi-automated fact-checking through Semantic Similarity and Natural Language Inference](https://mlsanity.com/paper/2110.14532/) (2021)
- [DISCO: Comprehensive and Explainable Disinformation Detection](https://mlsanity.com/paper/2203.04928/) (2022)
- [Labeling Synthetic Content: User Perceptions of Warning Label Designs for AI-generated Content on Social Media](https://mlsanity.com/paper/2503.05711/) (2025)
- [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)
- [Quote Erat Demonstrandum: A Web Interface for Exploring the Quotebank Corpus](https://mlsanity.com/paper/2207.03592/) (2022)
- [Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking](https://mlsanity.com/paper/2204.12263/) (2022)
- [Priberam at MESINESP Multi-label Classification of Medical Texts Task](https://mlsanity.com/paper/2105.05614/) (2021)

## Abstract (arXiv)

Fact checking is an essential task in journalism; its importance has been highlighted due to recently increased concerns and efforts in combating misinformation. In this paper, we present an automated fact-checking platform which given a claim, it retrieves relevant textual evidence from a document collection, predicts whether each piece of evidence supports or refutes the claim, and returns a final verdict. We describe the architecture of the system and the user interface, focusing on the choices made to improve its user-friendliness and transparency. We conduct a user study of the fact-checking platform in a journalistic setting: we integrated it with a collection of news articles and provide an evaluation of the platform using feedback from journalists in their workflow. We found that the predictions of our platform were correct 58% of the time, and 59% of the returned evidence was relevant.