Community Detection and Patient Experience Analysis in Reddit Conversations on Janus Kinase Inhibitors using Large Language Models
Journal:
medRxiv
Published Date:
Feb 4, 2026
Abstract
The emergence of Janus kinase (JAK) inhibitors, a relatively new class of medications for autoimmune and inflammatory conditions, has been accompanied by reports of adverse effects observed during clinical trials. However, uncertainty over their safety and efficacy in wider, unselected populations has led to discussion and speculation on social media such as Reddit. Social networks represent a novel, rich source of real-world pharmacovigilance data. They are also an environment where unverified information about these medications may circulate. This paper analyzes Reddit conversations related to JAK inhibitors, applying graph modeling and community detection techniques using Neo4j and the Louvain algorithm. Data from 2011 to 2024 were collected, cleaned, and used to construct a directed graph, incorporating posts, comments, users, and drug mentions as nodes and their interactions as edges. Advanced computational methods, including large language models, were utilized to analyze textual data and identify patient-reported experiences that diverge from current medical consensus. This study systematically maps online discourse and identifies key participants to understand how patient experiences and concerns about JAK inhibitors are shared within communities. The findings show that various subreddits serve as hubs of information in which key influencers are spreading both positive and negative information within the Reddit ecosystem. Highlighting the potential to integrate graph-based approaches, Neo4j, and advanced LLMs in real-time pharmacovigilance, this study presents compelling evidence of the emerging conversations surrounding JAK inhibitors and how they affect public health
