# Some Observations on Fact-Checking Work with Implications for Computational Support

arXiv: https://arxiv.org/abs/2305.02224 · HTML: https://mlsanity.com/paper/2305.02224/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 May 2023.

## Built on by (most cited first, 9 of 9)

- [Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI](https://mlsanity.com/paper/2308.07213/) (2023)
- [HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims](https://mlsanity.com/paper/2502.11753/) (2025)
- [When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits](https://mlsanity.com/paper/2503.03417/) (2025)
- [Multi-Layer Ranking with Large Language Models for News Source Recommendation](https://mlsanity.com/paper/2406.11745/) (2024)
- [Multilingual Models for Check-Worthy Social Media Posts Detection](https://mlsanity.com/paper/2408.06737/) (2024)
- [Generating Zero-shot Abstractive Explanations for Rumour Verification](https://mlsanity.com/paper/2401.12713/) (2024)
- [NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking](https://mlsanity.com/paper/2305.04825/) (2023)
- [Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool](https://mlsanity.com/paper/2507.06734/) (2025)
- [Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Online](https://mlsanity.com/paper/2609.00808/) (2026)

## Abstract (arXiv)

Social media and user-generated content (UGC) have become increasingly important features of journalistic work in a number of different ways. However, the growth of misinformation means that news organisations have had devote more and more resources to determining its veracity and to publishing corrections if it is found to be misleading. In this work, we present the results of interviews with eight members of fact-checking teams from two organisations. Team members described their fact-checking processes and the challenges they currently face in completing a fact-check in a robust and timely way. The former reveals, inter alia, significant differences in fact-checking practices and the role played by collaboration between team members. We conclude with a discussion of the implications for the development and application of computational tools, including where computational tool support is currently lacking and the importance of being able to accommodate different fact-checking practices.