# Birdwatch: Crowd Wisdom and Bridging Algorithms can Inform Understanding and Reduce the Spread of Misinformation

arXiv: https://arxiv.org/abs/2210.15723 · HTML: https://mlsanity.com/paper/2210.15723/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2022.

## Built on by (most cited first, 21 of 21)

- [Simulating Social Media Using Large Language Models to Evaluate Alternative News Feed Algorithms](https://mlsanity.com/paper/2310.05984/) (2023)
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- [Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation](https://mlsanity.com/paper/2510.11423/) (2025)
- [Customized large language models can outperform Community Notes in correcting misinformation](https://mlsanity.com/paper/2403.11169/) (2024)
- [AI and the Future of Digital Public Squares](https://mlsanity.com/paper/2412.09988/) (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)
- [Assessing the Potential of Generative Agents in Crowdsourced Fact-Checking](https://mlsanity.com/paper/2504.19940/) (2025)
- [The Gray Area: Characterizing Moderator Disagreement on Reddit](https://mlsanity.com/paper/2601.01620/) (2026)
- [HelloFresh: LLM Evaluations on Streams of Real-World Human Editorial Actions across X Community Notes and Wikipedia edits](https://mlsanity.com/paper/2406.03428/) (2024)
- [The Synthetic Media Shift: Tracking the Rise, Virality, and Detectability of AI-Generated Multimodal Misinformation](https://mlsanity.com/paper/2604.15372/) (2026)
- [Aligned: A Platform-based Process for Alignment](https://mlsanity.com/paper/2311.08706/) (2023)
- [Finding Common Ground in a Sea of Alternatives](https://mlsanity.com/paper/2603.16751/) (2026)
- [How Human Feedback Shapes AI-generated Community Notes](https://mlsanity.com/paper/2606.30905/) (2026)
- [Can NLP Tackle Hate Speech in the Real World? Stakeholder-Informed Feedback and Survey on Counterspeech](https://mlsanity.com/paper/2508.04638/) (2025)
- [Truth with a Twist: The Rhetoric of Persuasion in Professional vs. Community-Authored Fact-Checks](https://mlsanity.com/paper/2601.14105/) (2026)
- [Deliberative Curation: A Protocol for Multi-Agent Knowledge Bases](https://mlsanity.com/paper/2606.00007/) (2026)
- [Better with Experience: Self-Evolving LLM Agents for Evidence-Grounded Health Community Notes](https://mlsanity.com/paper/2606.02215/) (2026)
- [Community-Driven API and AI Writer Design for Openly Scaling Community Notes](https://mlsanity.com/paper/2609.40067/) (2026)

## Abstract (arXiv)

We present an approach for selecting objectively informative and subjectively helpful annotations to social media posts. We draw on data from on an online environment where contributors annotate misinformation and simultaneously rate the contributions of others. Our algorithm uses a matrix-factorization (MF) based approach to identify annotations that appeal broadly across heterogeneous user groups - sometimes referred to as "bridging-based ranking." We pair these data with a survey experiment in which individuals are randomly assigned to see annotations to posts. We find that annotations selected by the algorithm improve key indicators compared with overall average and crowd-generated baselines. Further, when deployed on Twitter, people who saw annotations selected through this bridging-based approach were significantly less likely to reshare social media posts than those who did not see the annotations.