AI-Driven Topic Modeling and Sentiment Analysis of Systemic Lupus Erythematosus Discussions on Social Media: Cross-Platform Study.

Background: Systemic lupus erythematosus (SLE) is a multifactorial autoimmune disease influenced by genetic, epigenetic, ecological, and environmental factors, with a global prevalence of 7.7 to 13 per 100,000, and standardized mortality rates of 2.4% to 5.9%. Between 14% and 75% of patients experience psychiatric comorbidities such as anxiety and depression, which impair treatment adherence and health-related quality of life. Social media has become an important channel for patients to express health concerns and seek support. Reddit and Weibo, as mainstream platforms globally and in China, respectively, host large volumes of user-generated content; however, no prior study has examined SLE-related discourse across both cultural and platform contexts. Objective: This study aimed to characterize SLE-related discussions on Reddit and Weibo. Specifically, we sought to identify and hierarchically categorize discussion topics, compare platform-specific differences in patient concerns, assess sentiment polarity across topics and thematic groups, and characterize public misconceptions related to SLE. Methods: This observational, computational content analysis examined all SLE-related discussions on Reddit (r/lupus) and Weibo up to November 30, 2024. An AI-driven pipeline first embedded the discussions using the Multilingual-E5-base BERT (bidirectional encoder representations from transformers) model, then applied uniform manifold approximation and projection (UMAP) for dimensionality reduction and hierarchical density-based spatial clustering of applications with noise (HDBSCAN) for density-based clustering to identify fine-grained topics, with keywords extracted via class-based term frequency-inverse document frequency (c-TF-IDF). Topics were further grouped into higher-level thematic domains through spectral clustering, with labels and definitions generated using GPT-5 Thinking via prompt engineering. Sentiment polarity (positive, neutral, and negative) was classified using a fine-tuned multilingual BERT model. Two clinical pharmacists independently validated the topic modeling and sentiment results. Results: We analyzed 11,318 discussions from 3649 unique authors on Reddit and 33,628 discussions from 21,509 authors on Weibo. We identified 99 fine-grained topics on Reddit and 189 on Weibo, grouped into 6 thematic domains per platform. Reddit discussions centered on diagnostic journeys, treatment experiences, and symptom management, whereas Weibo discussions emphasized social news, charitable activities, and traditional Chinese medicine (eg, artemisinin). The overall sentiment on Reddit was positive (mean 0.42, SD 0.86; 95% CI 0.40-0.43), whereas that on Weibo was neutral (mean 0.01, SD 0.91; 95% CI 0.00-0.02). Sentiment was positive in 7493, neutral in 1032, and negative in 2793 Reddit discussions, whereas on Weibo, it was positive, neutral, and negative in 14,026, 5768, and 13,834 discussions, respectively. Treatment-related misconceptions and unverified remedies recurred on both platforms. Conclusions: This cross-platform analysis reveals both shared and platform-specific concerns among people with SLE, highlighting distinct informational and emotional needs across cultural and platform contexts. Recurring treatment-related misconceptions and the substantial volume of negative-sentiment discussions warrant targeted public health communication and psychological support. The findings may help clinicians, public health authorities, and patient support organizations identify unmet needs, while the AI-driven approach offers a scalable, real-time tool for monitoring patient perspectives.