# Diffusion of Community Fact-Checked Misinformation on Twitter

arXiv: https://arxiv.org/abs/2205.13673 · HTML: https://mlsanity.com/paper/2205.13673/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2022.

## Built on by (most cited first, 7 of 7)

- [Reinforcement Learning-based Counter-Misinformation Response Generation: A Case Study of COVID-19 Vaccine Misinformation](https://mlsanity.com/paper/2303.06433/) (2023)
- [Evidence-Driven Retrieval Augmented Response Generation for Online Misinformation](https://mlsanity.com/paper/2403.14952/) (2024)
- [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)
- [Which linguistic cues make people fall for fake news? A comparison of cognitive and affective processing](https://mlsanity.com/paper/2312.03751/) (2023)
- [Truth with a Twist: The Rhetoric of Persuasion in Professional vs. Community-Authored Fact-Checks](https://mlsanity.com/paper/2601.14105/) (2026)
- [Why Does Misinformation Propagate Faster? An Algorithmic Perspective on X](https://mlsanity.com/paper/2609.28947/) (2026)

## Abstract (arXiv)

The spread of misinformation on social media is a pressing societal problem that platforms, policymakers, and researchers continue to grapple with. As a countermeasure, recent works have proposed to employ non-expert fact-checkers in the crowd to fact-check social media content. While experimental studies suggest that crowds might be able to accurately assess the veracity of social media content, an understanding of how crowd fact-checked (mis-)information spreads is missing. In this work, we empirically analyze the spread of misleading vs. not misleading community fact-checked posts on social media. For this purpose, we employ a dataset of community-created fact-checks from Twitter's Birdwatch pilot and map them to resharing cascades on Twitter. Different from earlier studies analyzing the spread of misinformation listed on third-party fact-checking websites (e.g., Snopes), we find that community fact-checked misinformation is less viral. Specifically, misleading posts are estimated to receive 36.62% fewer retweets than not misleading posts. A partial explanation may lie in differences in the fact-checking targets: community fact-checkers tend to fact-check posts from influential user accounts with many followers, while expert fact-checks tend to target posts that are shared by less influential users. We further find that there are significant differences in virality across different sub-types of misinformation (e.g., factual errors, missing context, manipulated media). Moreover, we conduct a user study to assess the perceived reliability of (real-world) community-created fact-checks. Here, we find that users, to a large extent, agree with community-created fact-checks. Altogether, our findings offer insights into how misleading vs. not misleading posts spread and highlight the crucial role of sample selection when studying misinformation on social media.