Some Observations on Fact-Checking Work with Implications for Computational Support
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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.
Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 3 May 2023.
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Citing papers per year: 2023: 2; 2024: 3; 2025: 3; 2026: 1.
9 papers.
- 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
- 2025HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claimscited by 4
- 2025When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Editscited by 3
- 2024Multi-Layer Ranking with Large Language Models for News Source Recommendationcited by 2
- 2024Multilingual Models for Check-Worthy Social Media Posts Detectioncited by 2
- 2024Generating Zero-shot Abstractive Explanations for Rumour Verificationcited by 1
- 2023NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checkingcited by 0
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Toolcited by 0
- 2026Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Onlinecited by 0
- 2026Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Online1 Sep 2026
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool9 Jul 2025
- 2025When Claims Evolve: Evaluating and Enhancing the Robustness of Embedding Models Against Misinformation Edits5 Mar 2025
- 2025HintsOfTruth: A Multimodal Checkworthiness Detection Dataset with Real and Synthetic Claims17 Feb 2025
- 2024Multilingual Models for Check-Worthy Social Media Posts Detection13 Aug 2024
- 2024Multi-Layer Ranking with Large Language Models for News Source Recommendation17 Jun 2024
- 2024Generating Zero-shot Abstractive Explanations for Rumour Verification23 Jan 2024
- 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI14 Aug 2023
- 2023NewsQuote: A Dataset Built on Quote Extraction and Attribution for Expert Recommendation in Fact-Checking5 May 2023