Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation

Community Notes, the crowd-sourced misinformation governance system on X (formerly Twitter), allows users to flag misleading posts, attach contextual notes, and rate the notes' helpfulness. However, our empirical analysis of 30.8K health-related notes reveals substantial latency, with a median delay of 17.6 hours before notes receive a helpfulness status. To improve responsiveness during real-world misinformation surges, we propose CROWDNOTES+, a unified LLM-based framework that augments Community Notes for faster and more reliable health misinformation governance. CROWD-NOTES+ integrates two modes: (1) evidencegrounded note augmentation and (2) utilityguided note automation, supported by a hierarchical three-stage evaluation of relevance, correctness, and helpfulness. We instantiate the framework with HEALTHNOTES, a benchmark of 1.2K health notes annotated for helpfulness, and a fine-tuned helpfulness judge. Our analysis first uncovers a key loophole in current crowd-sourced governance: voters frequently conflate stylistic fluency with factual accuracy. Addressing this via our hierarchical evaluation, experiments across 15 representative LLMs demonstrate that CROWDNOTES+ significantly outperforms human contributors in note correctness, helpfulness, and evidence utility. 1