Abstract 4129223: Large Language Modeling-Enabled Analysis of Atrial Fibrillation Topics and Sentiments on Social Media

Purpose: We utilized large language modeling (LLM) and automated topic modeling techniques to describe public perceptions regarding AF on Reddit, a social media platform. Methods: In this qualitative study, we curated all AF-related discussions on Reddit from 2006 to 2023 by searching for all posts and comments containing AF-related terms (“atrial fibrillation”, “afib”, and “atrial flutter”). Discussions were embedded using a state-of-the-art embedding model (BAAI/bge-base-en-v1.5). Embeddings are reduced in dimensionality using Uniform Manifold Approximation and Project and clustered using K-Means Clustering to find topics and groups. Topic and group labels are enhanced using large language models (Llama2 and GPT4, respectively). Results: We identified a total of 86,323 discussions related to AF from 38,183 unique users between April 14, 2006 and November 20, 2023. Discussions around wearables and ablations increased over time, while discussions around anticoagulation had periodic spikes. The topic modeling pipeline automatically identified 64 topics and 9 groups (Figure 1). Groups emphasized the following themes: lifestyle factors and triggers associated with AF (groups 1, 5, 7); patient experiences with management strategies including anticoagulation, rate control, pharmacological rhythm control, and ablation (groups 2, 4, 5); experiences with wearable devices (group 8); psychological burden of living with AF (group 6); and online advertisements for AF-related medications (groups 3, 9). Conclusions: Our AI-enabled topic modeling analysis revealed public perceptions about AF including triggers, experiences with different management strategies, impact of wearables, and psychological burden of the diagnosis, which may help guide potential strategies for improving shared decision making around the AF patient journey.