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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. Author Summary People often turn to Reddit to share their experiences with medications, including Janus kinase (JAK) inhibitors, which are used to treat autoimmune conditions such as arthritis, eczema, and alopecia areata. These drugs are fairly recent, and some safety concerns have been identified, making discussions about them on the internet a mixture of personal stories, questions, and statements which may not correspond to serious or established medical literature. [60] In this research we analyzed over ten years of Reddit discussions to explore how individuals discuss JAK inhibitors and how both correct and possibly misleading information circulates within groups. We integrated graph-based techniques, which illustrate the connections among users and conversations with AI tools that identify claims at odds, with clinical guidelines. We applied the term ”divergent patient experiences” exclusively to comments that contradict regulation or evidence-based sources, while personal accounts and feelings of individuals are not classified as divergent patient experiences. Our findings demonstrate that a very small number of users initiate a large proportion of conversations and discussions tend to revolve around the main health topics. This approach of using social media to monitor public health opinions shows the manner in which it avails information regarding real patient concerns, but it also shows the requirement of expert supervision when using AI to appraise health information being shared online.