# Can Community Notes Replace Professional Fact-Checkers?

arXiv: https://arxiv.org/abs/2502.14132 · HTML: https://mlsanity.com/paper/2502.14132/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 19 Feb 2025.

## Builds on (most cited first, 9 of 9)

- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking](https://mlsanity.com/paper/2502.09083/) (2025)
- [Human and technological infrastructures of fact-checking](https://mlsanity.com/paper/2205.10894/) (2022)
- [Crowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?](https://mlsanity.com/paper/2208.09214/) (2022)
- [Community-Based Fact-Checking on Twitter's Birdwatch Platform](https://mlsanity.com/paper/2104.07175/) (2021)
- [What Makes Medical Claims (Un)Verifiable? Analyzing Entity and Relation Properties for Fact Verification](https://mlsanity.com/paper/2402.01360/) (2024)
- [Who Checks the Checkers? Exploring Source Credibility in Twitter's Community Notes](https://mlsanity.com/paper/2406.12444/) (2024)
- [Community Fact-Checks Trigger Moral Outrage in Replies to Misleading Posts on Social Media](https://mlsanity.com/paper/2409.08829/) (2024)
- [Community Notes vs. Snoping: How the Crowd Selects Fact-Checking Targets on Social Media](https://mlsanity.com/paper/2305.09519/) (2023)

## Built on by (most cited first, 8 of 8)

- [Community Moderation and the New Epistemology of Fact Checking on Social Media](https://mlsanity.com/paper/2505.20067/) (2025)
- [COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations](https://mlsanity.com/paper/2510.24810/) (2025)
- [The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation](https://mlsanity.com/paper/2604.15372/) (2026)
- [The Psychology of Falsehood: A Human-Centric Survey of Misinformation Detection](https://mlsanity.com/paper/2509.15896/) (2025)
- [Towards Multi-Agent-Simulation-Based Community Note Evaluation](https://mlsanity.com/paper/2606.18268/) (2026)
- [Gaming Consensus: Coordinated Manipulation in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2607.01824/) (2026)
- [One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread](https://mlsanity.com/paper/2608.30311/) (2026)
- [Community-Driven API and AI Writer Design for Openly Scaling Community Notes](https://mlsanity.com/paper/2609.40067/) (2026)

## Abstract (arXiv)

Two commonly employed strategies to combat the rise of misinformation on social media are (i) fact-checking by professional organisations and (ii) community moderation by platform users. Policy changes by Twitter/X and, more recently, Meta, signal a shift away from partnerships with fact-checking organisations and towards an increased reliance on crowdsourced community notes. However, the extent and nature of dependencies between fact-checking and helpful community notes remain unclear. To address these questions, we use language models to annotate a large corpus of Twitter/X community notes with attributes such as topic, cited sources, and whether they refute claims tied to broader misinformation narratives. Our analysis reveals that community notes cite fact-checking sources up to five times more than previously reported. Fact-checking is especially crucial for notes on posts linked to broader narratives, which are twice as likely to reference fact-checking sources compared to other sources. Our results show that successful community moderation relies on professional fact-checking and highlight how citizen and professional fact-checking are deeply intertwined.