True or False: Studying the Work Practices of Professional Fact-Checkers
Open accessAbstract
Misinformation has developed into a critical societal threat that can lead to disastrous societal consequences. Although fact-checking plays a key role in combating misinformation, relatively little research has empirically investigated work practices of professional fact-checkers. To address this gap, we conducted semi-structured interviews with 21 fact-checkers from 19 countries. The participants reported being inundated with information that needs filtering and prioritizing prior to fact-checking. The interviews surfaced a pipeline of practices fragmented across disparate tools that lack integration. Importantly, fact-checkers lack effective mechanisms for disseminating the outcomes of their efforts which prevents their work from fully achieving its potential impact. We found that the largely manual and labor intensive nature of current fact-checking practices is a barrier to scale. We apply these findings to propose a number of suggestions that can improve the effectiveness, efficiency, scale, and reach of fact-checking work and its outcomes.
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Research has demonstrated fact-checking's efficacy in helping debunk fake news but also acknowledged that the labor-intensive nature made it challenging to scale.
“Research has demonstrated its efcacy in helping debunk fake news [19,102] but also acknowledged that the labor-intensive nature made it challenging to scale [71].”
From Synthetic Lies: Understanding AI-Generated Misinformation and Evaluating Algorithmic and Human Solutions (2023) · cites this paper as [71]
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- Authors
- Micallef, N., Armacost, V., Memon, N., & Patil, S.
- Affiliations
- New York University Abu Dhabi (AE)
- Indiana University Bloomington (US)
- New York University (US)
- University of Utah (US)
- Venue
- Proceedings of the ACM on Human-Computer Interaction
- Published
- 2022-03-30
- Volume
- 6
- Issue
- CSCW1
- Pages
- 1-44
- ISSN
- 2573-0142 (ISSN-L)
- OpenAlex
- W4225879562
- Cited by
- 70
- References
- 113
- Citations by year
- 2026: 17 · 2025: 20 · 2024: 23 · 2023: 8 · 2022: 2
- FWCI
- 19.07
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Proceedings of the ACM on Human-Computer Interaction (publishedVersion, cc-by-nc-sa)
- Funders
- Indiana University Bloomington
- Topics
- Social Sciences › Sociology and Political Science › Misinformation and Its Impacts
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Social Sciences › Decision Sciences › Information Systems and Management › Academic Publishing and Open Access
- Keywords
- fact-checking, misinformation, work practices, information filtering, priority setting, scalability, manual labor, human-in-the-loop
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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