How Human Feedback Shapes AI-generated Community Notes
Community Notes, a bridging-based crowd-sourced fact-checking system, has emerged as a new mechanism for moderating misleading information on social media and has been adopted by major platforms including X, Facebook, Instagram, Threads, and TikTok. Since its introduction, there has been an open question about what role AI could play in scaling and optimizing the system. Recently, X extended its Community Notes system by introducing Collaborative Notes: notes initially drafted by an LLM and iteratively refined based on feedback from human contributors. In this work, we systematically analyze the complete corpus of 19,146 collaborative notes and 211,850 instances of human feedback. First, ...
| Authors | Soham De, Isaac Slaughter, Jiawei Guo et al. |
|---|---|
| Primary category | cs.CY |
| Published | 29 June 2026 |
| Last updated at source | 29 June 2026 |
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