Human and technological infrastructures of fact-checking
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Abstract (arXiv)
Increasing demands for fact-checking has led to a growing interest in developing systems and tools to automate the fact-checking process. However, such systems are limited in practice because their system design often does not take into account how fact-checking is done in the real world and ignores the insights and needs of various stakeholder groups core to the fact-checking process. This paper unpacks the fact-checking process by revealing the infrastructures -- both human and technological -- that support and shape fact-checking work. We interviewed 26 participants belonging to 16 fact-checking teams and organizations with representation from 4 continents. Through these interviews, we describe the human infrastructure of fact-checking by identifying and presenting, in-depth, the roles of six primary stakeholder groups, 1) Editors, 2) External fact-checkers, 3) In-house fact-checkers, 4) Investigators and researchers, 5) Social media managers, and 6) Advocators. Our findings highlight that the fact-checking process is a collaborative effort among various stakeholder groups and associated technological and informational infrastructures. By rendering visibility to the infrastructures, we reveal how fact-checking has evolved to include both short-term claims centric and long-term advocacy centric fact-checking. Our work also identifies key social and technical needs and challenges faced by each stakeholder group. Based on our findings, we suggest that improving the quality of fact-checking requires systematic changes in the civic, informational, and technological contexts.
Key facts. Cited by 16 papers in the map (data through 13 Oct 2026). First posted on arXiv 22 May 2022.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 2; 2025: 5; 2026: 4.
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- 2023Scientific Fact-Checking: A Survey of Resources and Approachescited by 53
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
- 2023Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?cited by 21
- 2024The Impact and Opportunities of Generative AI in Fact-Checkingcited by 10
- 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
- 2025Can Community Notes Replace Professional Fact-Checkers?cited by 8
- 2022Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systemscited by 7
- 2025Can LLMs Automate Fact-Checking Article Writing?cited by 7
- 2024The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizationscited by 3
- 2025Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicinecited by 2
- 2026Co-FactChecker: A Framework for Human-AI Collaborative Claim Verification Using Large Reasoning Modelscited by 1
- 2022Graphical Models of False Information and Fact Checking Ecosystemscited by 0
4 more
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Toolcited by 0
- 2026Towards Multi-Agent-Simulation-Based Community Note Evaluationcited by 0
- 2026Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Onlinecited by 0
- 2026SoK: Formal Methods for Fact-Checking and Information Integritycited by 0
- 2026SoK: Formal Methods for Fact-Checking and Information Integrity19 Sep 2026
- 2026Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Online1 Sep 2026
- 2026Towards Multi-Agent-Simulation-Based Community Note Evaluation3 Jun 2026
- 2026Co-FactChecker: A Framework for Human-AI Collaborative Claim Verification Using Large Reasoning Models15 Apr 2026
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool9 Jul 2025
- 2025Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine25 Jun 2025
- 2025Can LLMs Automate Fact-Checking Article Writing?22 Mar 2025
- 2025Can Community Notes Replace Professional Fact-Checkers?19 Feb 2025
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking13 Feb 2025
- 2024The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizations4 Aug 2024
- 2024The Impact and Opportunities of Generative AI in Fact-Checking24 May 2024
- 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI14 Aug 2023
4 more
- 2023Scientific Fact-Checking: A Survey of Resources and Approaches26 May 2023
- 2023Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?3 Apr 2023
- 2022Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systems22 Nov 2022
- 2022Graphical Models of False Information and Fact Checking Ecosystems24 Aug 2022
- 2023Scientific Fact-Checking: A Survey of Resources and Approachescited by 53
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checkingcited by 29
- 2023Does Human Collaboration Enhance the Accuracy of Identifying LLM-Generated Deepfake Texts?cited by 21
- 2024The Impact and Opportunities of Generative AI in Fact-Checkingcited by 10
- 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AIcited by 9
- 2025Can Community Notes Replace Professional Fact-Checkers?cited by 8
- 2022Autonomation, Not Automation: Activities and Needs of European Fact-checkers as a Basis for Designing Human-Centered AI Systemscited by 7
- 2025Can LLMs Automate Fact-Checking Article Writing?cited by 7
- 2024The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizationscited by 3
- 2025Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicinecited by 2
- 2022Graphical Models of False Information and Fact Checking Ecosystemscited by 0
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Toolcited by 0
12 most cited of 15 All 15 (JSON)
- 2026SoK: Formal Methods for Fact-Checking and Information Integrity19 Sep 2026
- 2026Ctrl-F-Resist. Practices, Challenges, and Technical Needs of Civil Society Organizations Monitoring the Far-Right Online1 Sep 2026
- 2026Towards Multi-Agent-Simulation-Based Community Note Evaluation3 Jun 2026
- 2025Civil Society in the Loop: Feedback-Driven Adaptation of (L)LM-Assisted Classification in an Open-Source Telegram Monitoring Tool9 Jul 2025
- 2025Decide less, communicate more: On the construct validity of end-to-end fact-checking in medicine25 Jun 2025
- 2025Can LLMs Automate Fact-Checking Article Writing?22 Mar 2025
- 2025Can Community Notes Replace Professional Fact-Checkers?19 Feb 2025
- 2025Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking13 Feb 2025
- 2024The Implications of Open Generative Models in Human-Centered Data Science Work: A Case Study with Fact-Checking Organizations4 Aug 2024
- 2024The Impact and Opportunities of Generative AI in Fact-Checking24 May 2024
- 2023Human-centered NLP Fact-checking: Co-Designing with Fact-checkers using Matchmaking for AI14 Aug 2023
- 2023Scientific Fact-Checking: A Survey of Resources and Approaches26 May 2023
12 newest of 15 All 15 (JSON)