Large Language Modeling–Enabled Analysis of Atrial Fibrillation on Social Media

Abstract

BACKGROUND: Atrial fibrillation (AF) is the most common arrhythmia worldwide, and patient perceptions significantly influence shared treatment decisions. Artificial intelligence–driven analysis of social media may offer valuable insights into contemporary public attitudes toward AF outside clinical settings. METHODS: This qualitative study used large language modeling and advanced artificial intelligence topic modeling techniques to analyze public perceptions of AF from Reddit discussions between April 2006 and November 2023. RESULTS: We curated 86 323 AF-related conversations (18 754 posts, 67 569 comments) across 38 183 unique users by searching terms related to AF. Our topic modeling identified 65 distinct discussion topics organized into 9 thematic groups, with topics including personal experiences with treatments (eg, ablation, rate versus rhythm control), roles of health care providers and community support, AF triggers (diet, illicit substances, supplements, stress, caffeine), and anecdotes highlighting the difficulties of living with AF. Discussions commonly reflected 3 main themes: (1) advantages and limitations of wearable devices for AF monitoring, (2) hesitancy and misconceptions about AF treatment, and (3) patient-centered challenges following an AF diagnosis. CONCLUSIONS: The artificial intelligence–enabled analysis underscored substantial public discourse around patient experiences with AF detection and management. Leveraging social media data to understand patient perspectives on cardiovascular health may inform patient-centered resources and future research directions to better support patients living with AF.