# Misinformation as a harm: structured approaches for fact-checking prioritization

arXiv: https://arxiv.org/abs/2312.11678 · HTML: https://mlsanity.com/paper/2312.11678/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2023.

## Built on by (most cited first, 5 of 5)

- [Sword and Shield: Uses and Strategies of LLMs in Navigating Disinformation](https://mlsanity.com/paper/2506.07211/) (2025)
- [Diverse, but Divisive: LLMs Can Exaggerate Gender Differences in Opinion Related to Harms of Misinformation](https://mlsanity.com/paper/2401.16558/) (2024)
- [TrendFact: A Benchmark Towards Hotspot Perception in Automatic Fact-Checking](https://mlsanity.com/paper/2410.15135/) (2024)
- [What do people want to fact-check?](https://mlsanity.com/paper/2602.10935/) (2026)
- [Beyond Binary Detection: A Multi-Dimensional Taxonomy of Cancer Misinformation on Reddit](https://mlsanity.com/paper/2607.12383/) (2026)

## Abstract (arXiv)

In this work, we examine how fact-checkers prioritize which claims to fact-check and what tools may assist them in their efforts. Through a series of interviews with 23 professional fact-checkers from around the world, we validate that harm assessment is a central component of how fact-checkers triage their work. We also clarify the processes behind fact-checking prioritization, finding that they are typically ad hoc, and gather suggestions for tools that could help with these processes. To address the needs articulated by fact-checkers, we present a structured framework of questions to help fact-checkers negotiate the priority of claims through assessing potential harms. Our FABLE Framework of Misinformation Harms incorporates five dimensions of magnitude -- (social) Fragmentation, Actionability, Believability, Likelihood of spread, and Exploitativeness -- that can help determine the potential urgency of a specific message or claim when considering misinformation as harm. The result is a practical and conceptual tool to support fact-checkers and others as they make strategic decisions to prioritize their efforts. We conclude with a discussion of computational approaches to support structured prioritization, as well as applications beyond fact-checking to content moderation and curation.