# Community Moderation and the New Epistemology of Fact Checking on Social Media

arXiv: https://arxiv.org/abs/2505.20067 · HTML: https://mlsanity.com/paper/2505.20067/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 79 references that are in the map.
- First posted on arXiv 26 May 2025.

## Builds on (most cited first, 25 of 25)

- [Automated Fact-Checking for Assisting Human Fact-Checkers](https://mlsanity.com/paper/2103.07769/) (2021)
- [Factuality Challenges in the Era of Large Language Models](https://mlsanity.com/paper/2310.05189/) (2023)
- [That is a Known Lie: Detecting Previously Fact-Checked Claims](https://mlsanity.com/paper/2005.06058/) (2020)
- [Uncovering Coordinated Networks on Social Media: Methods and Case Studies](https://mlsanity.com/paper/2001.05658/) (2020)
- [Show Me the Work: Fact-Checkers' Requirements for Explainable Automated Fact-Checking](https://mlsanity.com/paper/2502.09083/) (2025)
- [Fact-checking information from large language models can decrease headline discernment](https://mlsanity.com/paper/2308.10800/) (2023)
- [Supernotes: Driving Consensus in Crowd-Sourced Fact-Checking](https://mlsanity.com/paper/2411.06116/) (2024)
- [Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation](https://mlsanity.com/paper/2210.15723/) (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)
- [HateDay: Insights from a Global Hate Speech Dataset Representative of a Day on Twitter](https://mlsanity.com/paper/2411.15462/) (2024)
- [Justice in Misinformation Detection Systems: An Analysis of Algorithms, Stakeholders, and Potential Harms](https://mlsanity.com/paper/2204.13568/) (2022)
- [Automated Content Moderation Increases Adherence to Community Guidelines](https://mlsanity.com/paper/2210.10454/) (2022)
- [Customized large language models can outperform Community Notes in correcting misinformation](https://mlsanity.com/paper/2403.11169/) (2024)
- [Can Community Notes Replace Professional Fact-Checkers?](https://mlsanity.com/paper/2502.14132/) (2025)
- [Social Influence and Unfollowing Accelerate the Emergence of Echo Chambers](https://mlsanity.com/paper/1905.03919/) (2019)
- [Diffusion of Community Fact-Checked Misinformation on Twitter](https://mlsanity.com/paper/2205.13673/) (2022)
- [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)
- [Blackmarket-driven Collusion on Online Media: A Survey](https://mlsanity.com/paper/2008.13102/) (2020)
- [Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2504.19940/) (2025)
- [Can crowdsourcing rescue the social marketplace of ideas?](https://mlsanity.com/paper/2104.13754/) (2021)
- [Political Fact-Checking Efforts are Constrained by Deficiencies in Coverage, Speed, and Reach](https://mlsanity.com/paper/2412.13280/) (2024)
- [References to unbiased sources increase the helpfulness of community fact-checks](https://mlsanity.com/paper/2503.10560/) (2025)
- [Factors in Recommending Contrarian Content on Social Media](https://mlsanity.com/paper/1705.06597/) (2017)

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

- [COMMUNITYNOTES: A Dataset for Exploring the Helpfulness of Fact-Checking Explanations](https://mlsanity.com/paper/2510.24810/) (2025)
- [The Generative AI Paradox: GenAI and the Erosion of Trust, the Corrosion of Information Verification, and the Demise of Truth](https://mlsanity.com/paper/2601.00306/) (2026)
- [The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation](https://mlsanity.com/paper/2604.15372/) (2026)
- [Truth with a Twist: The Rhetoric of Persuasion in Professional vs. Community-Authored Fact-Checks](https://mlsanity.com/paper/2601.14105/) (2026)
- [Whose Judgments Count? Representation Gaps in Crowdsourced Content Moderation Produce Unequal Protection from Perceived Toxicity](https://mlsanity.com/paper/2609.01625/) (2026)

## Abstract (arXiv)

Social media platforms have traditionally relied on internal moderation teams and partnerships with independent fact-checking organizations to identify and flag misleading content. Recently, however, platforms including X (formerly Twitter) and Meta have shifted towards community-driven content moderation by launching their own versions of crowd-sourced fact-checking -- Community Notes. If effectively scaled and governed, such crowd-checking initiatives have the potential to combat misinformation with increased scale and speed as successfully as community-driven efforts once did with spam. Nevertheless, general content moderation, especially for misinformation, is inherently more complex. Public perceptions of truth are often shaped by personal biases, political leanings, and cultural contexts, complicating consensus on what constitutes misleading content. This suggests that community efforts, while valuable, cannot replace the indispensable role of professional fact-checkers. Here we systemically examine the current approaches to misinformation detection across major platforms, explore the emerging role of community-driven moderation, and critically evaluate both the promises and challenges of crowd-checking at scale.