{"ID":2856370,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.11423","arxiv_id":"2510.11423","title":"Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation","abstract":"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. CrowdNotes+ integrates two modes: (1) evidence-grounded note augmentation and (2) utility-guided 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.","short_abstract":"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 befor...","url_abs":"https://arxiv.org/abs/2510.11423","url_pdf":"https://arxiv.org/pdf/2510.11423v3","authors":"[\"Jiaying Wu\",\"Zihang Fu\",\"Haonan Wang\",\"Fanxiao Li\",\"Jiafeng Guo\",\"Preslav Nakov\",\"Min-Yen Kan\"]","published":"2025-10-13T13:57:23Z","proceeding":"cs.SI","tasks":"[\"cs.SI\",\"cs.CL\"]","methods":"[\"Large Language Model\"]","has_code":false}