Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation
Abstract
An empirical analysis of 30.8K health-related Community Notes on X finds a median delay of 17.6 hours before a note receives a helpfulness status. The paper proposes CrowdNotes+, an LLM-based framework that combines evidence-grounded note augmentation with utility-guided note automation, assessed through a hierarchical evaluation of relevance, correctness, and helpfulness on HealthNotes, a benchmark of 1.2K annotated health notes.
Type
Publication
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Fanxiao Li
CSC Visiting Student (Sep ‘25)
Visiting student; interests include Multimodal Misinformation, Large Vision-Language Models.
Min-Yen Kan
Associate Professor
WING lead; interests include Digital Libraries, Information Retrieval and Natural Language Processing.