All data used in this study were obtained from publicly accessible, anonymized sources on Reddit and Weibo. Ethical approval was obtained from the Guilin People’s Hospital Ethics Committee (2025-317KY).

Data were collected from Reddit and Weibo up to November 30, 2024. On Reddit, SLE-related communities were identified using the keywords “systemic lupus erythematosus,” “lupus erythematosus,” “lupus,” and “SLE.” Most of the keywords returned only r/lupus, whereas “lupus” returned 15 subreddits; however, all except r/lupus had weekly activity below 100 (mostly in the single digits) and were therefore excluded. All posts and comments from r/lupus were retrieved via the official Reddit API on January 20, 2025. As Weibo has no official API, a web crawler collected SLE-related posts and comments on January 10, 2025, using the Chinese keywords “系统性红斑狼疮” (systemic lupus erythematosus), “红斑狼疮” (lupus erythematosus), and “狼疮” (lupus). “SLE” was not used as a Weibo search term because it retrieved large volumes of unrelated content (eg, referring to regions rather than the disease). Posts deleted by users or removed by the platform before retrieval could not be obtained and were excluded.

Because posts and comments on Weibo cannot be reliably distinguished, posts and comments on both platforms were uniformly treated as individual “discussions” and used as the basic analytical unit to enable cross-platform comparison. The unstructured text was preprocessed using regular expressions to remove emojis, URLs, user mentions (“@”), extraneous whitespace, and stop words; exact duplicate discussions were removed via string matching.

BERTopic [26] (bidirectional encoder representations from transformers) was applied to the preprocessed dataset to identify discussion topics related to SLE. This state-of-the-art NLP method begins by embedding text using the pretrained sentence-level BERT model Multilingual-E5-base. The Multilingual-E5-base model was trained on more than 1 billion text pairs from multilingual datasets, including Reddit and Semantic Scholar Open Research Corpus (S2ORC; a large corpus containing more than 12.8 million scholarly papers in the biomedical domain) [27], making it particularly suitable for analyzing medical discourse expressed in both Chinese and English social media. Detailed methodological and parameter settings are provided in eMethods in Multimedia Appendix 1.

High-dimensional text embeddings were first reduced using uniform manifold approximation and projection (UMAP) to preserve the semantic structure, and then clustered using hierarchical density-based spatial clustering of applications with noise (HDBSCAN), which handles clusters of varying density and assigns points below the minimum cluster-size threshold to a noise class; these noise documents were excluded from subsequent analyses. Representative keywords for each topic were extracted using class-based term frequency-inverse document frequency (c-TF-IDF). A grid search optimized 4 parameters influencing topic quality—the UMAP n_neighbors and n_components, the HDBSCAN min_cluster_size, and the BERTopic nr_topics (Table S1 in Multimedia Appendix 1), selecting the configuration yielding the most coherent and well-separated topics.

Because BERTopic typically generates many granular topics, an additional clustering step was used to improve interpretability. Pairwise semantic similarities computed from the c-TF-IDF representations were reduced via UMAP and grouped into broader thematic groups using spectral clustering, which partitions topics according to the similarity-graph structure and captures nonconvex relationships that conventional methods may miss. This yielded a 2-level structure: fine-grained topics at the document level and higher-order thematic categories for interpretation. The optimal number of thematic groups was determined using the Silhouette coefficient (values approaching 1.0 indicate well-defined partitions [28]) and the Davies-Bouldin index (lower values indicate better clustering [29]). The configuration yielding the most favorable combination of both indices was selected.

To enhance the interpretability of these thematic groups, we used a large language model (LLM) as part of an innovative analytical approach. Specifically, the c-TF-IDF keywords and a representative subset of discussions for each group, both produced by BERTopic, were provided to GPT-5 Thinking (see eMethods in Multimedia Appendix 1 for details) via the following prompt:

The generated theme names were then reviewed by one of the authors (JT), who revised one label: “Lupus diagnosis and daily struggles” was changed to “Navigating lupus diagnosis and daily struggles” to better capture the active and ongoing nature of patients’ experiences in seeking a diagnosis and managing daily treatment, rather than a static description.

Sentiment analysis used bert-base-multilingual-cased [30], a multilingual BERT variant pretrained on 104 languages, including Chinese and English, with reliable performance on multilingual sentiment classification [31]. The model classified SLE-related discussions into 3 categories—positive, neutral, and negative—following the standard sentiment polarity framework [32,33] (operational definitions in Multimedia Appendix 2). It was fine-tuned on 2 public 3-class corpora: a Chinese news sentiment dataset (chinese-news-sentiment-c3-ds, ModelScope) and the English Coronavirus Tweets NLP dataset (Coronavirus tweets NLP-Text Classification, Kaggle); parameter settings are provided in Table S2 in Multimedia Appendix 1. The Softmax layer output positive, neutral, and negative classes, assigned values of 1, 0, and −1, respectively. For each topic and thematic group, the sentiment score was the arithmetic mean across all discussions within it, with values closer to 1, 0, and −1 indicating predominantly positive, neutral, and negative orientations. The model achieved an F₁-score of 88.52% on the combined test set, meeting deployment requirements.

All validation was performed by 2 clinical pharmacists (JP and HJ) working independently, with disagreements resolved by author JT. For topic validation, 30% of topics were randomly sampled from each platform (30 from Reddit, 57 from Weibo; 87 in total), with the top 10 c-TF-IDF keywords and 5 posts extracted per topic (435 total posts; Cohen κ=0.79). All 12 higher-level thematic groups (6 Reddit and 6 Weibo) were similarly reviewed, with 25 posts extracted per group (300 total posts; Cohen κ=0.82). For sentiment validation, 300 discussions were randomly selected (25 per thematic group); interrater agreement was 0.76 (Cohen κ), and manual-model agreement was 79.2%. These findings support the reliability of the sentiment classification, though proportions should be interpreted as general trends rather than precise estimates.

This study systematically analyzed SLE-related content on Reddit and Weibo using AI-based methods, identifying 99 and 189 topics from 11,318 Reddit and 33,628 Weibo discussions, respectively, with each platform’s topic further clustered into 6 thematic groups. Both platforms converged on core clinical and psychosocial themes, including symptom management, medication side effects, and emotional support, yet diverged in emphasis. Reddit discussions centered on individualized diagnosis and treatment management, whereas Weibo discussions gave greater visibility to collective, socially oriented narratives around charity and integrative medicine such as TCM (eg, artemisinin). Because the platforms differ in user composition, moderation practices, linguistic contexts, and communication styles, these differences likely reflect a combination of sampling, platform-specific, and cultural factors rather than culture alone. Moreover, social media users are a self-selected subgroup whose concerns may not fully represent the broader SLE population.

Consistent with previous studies, users on TikTok and 13 other English-language social media platforms focus on daily symptom management [34,35]; YouTube videos emphasize information quality and health education [36]; and vertical communities, such as PatientsLikeMe, center discussions around symptoms and quality of life [37]. Together, this evidence underscores the platform-specific roles that different media play within the broader disease information ecosystem, suggesting that public health education and social media–based interventions should be tailored to the user base and communication style of each platform.

Sentiment analysis showed an overall positive tendency on Reddit (mean 0.42, SD 0.85; 95% CI 0.40-0.43) and a neutral tendency on Weibo (mean 0.01, SD 0.91; 95% CI 0.00-0.02; Figure 4). More clinically meaningful than these platform-level averages were the negative clusters within specific topics and thematic groups, notably pregnancy-related and postpartum-related discussions on Reddit (topic 78) and anger, intergenerational conflict, and Chinese medicine–oriented discussions on Weibo (topic 188 and group 0). People with SLE are known to experience high rates of depression, anxiety, and fatigue [38], which are closely associated with disease activity, medication side effects, and insufficient social support. TikTok research has likewise shown that pharmacological treatments are portrayed negatively [34], and 17.2% of people with SLE reported negative emotions during the COVID-19 pandemic that were linked to social media content [39]. These findings highlight emotionally vulnerable subgroups among people with SLE and the need for targeted psychological support around fertility, family relationships, and major life events, as well as ongoing monitoring of panic and misinformation on such platforms. Building on frameworks such as online communities and patient registries proposed by Blackie et al [40], integrating social media with AI-driven analysis offers valuable opportunities for people with SLE to better manage their conditions and to understand public perceptions and unmet needs [35,40].

Frequent discussions of TCM and herbal therapies in the Weibo corpus suggest that these approaches were salient among Chinese-speaking users with SLE. This interest may stem from concerns about long-term adverse effects of conventional therapies, heightened by the chronic, relapsing nature of SLE that necessitates lifelong treatment, together with culturally rooted health beliefs. Active compounds in TCM, such as artemisinin, have shown potential in modulating immune function and reducing disease activity [41,42], and TCM is often perceived as a gentler, complementary approach. A Chinese cross-sectional study of 351 people with SLE found that 85.5% regularly used at least 1 complementary therapy [43]. These preferences may also reflect structural factors in China’s health care system: approximately 85.3% of rheumatologists practice in tertiary hospitals concentrated in urban centers [44], creating access barriers in less-developed regions and increasing reliance on online health information [12-14]. By contrast, Reddit users within Western health care systems more frequently sought diagnostic clarification and engaged in treatment decision-making, reflecting the role of online communities in supporting informational needs and self-management in chronic conditions [45]. As patient motivations, treatment behaviors, and health-system influences were not directly assessed, these interpretations should be regarded as plausible contextual factors rather than definitive conclusions.

Moreover, cross-platform differences should not be attributed solely to cultural or health-system factors. Research suggests that differences in platform affordances, user communities, and health-system contexts shape not only what health topics are discussed but also how illness experiences are framed and shared [46]. Weibo’s algorithmic curation and hashtag-based aggregation increase the visibility of thematically related and culturally familiar content, shaping how users identify and prioritize health information. Reddit’s threaded format and community norms, by contrast, foster more deliberative, evidence-oriented exchanges that direct users toward procedural information. These structural features indicate that platform design does not merely reflect preexisting user preferences but actively shapes the information environments users encounter, the credibility cues they rely on, and ultimately the illness narratives they construct and share.

Discussions surrounding SLE on both Reddit and Weibo increased markedly between 2019 and 2021. This surge coincided with the COVID-19 pandemic. During this period, people with SLE frequently reported feelings of losing control over their health, accompanied by heightened anxiety and stress [47], while overall news consumption and social media use also rose significantly [39,48]. Notably, at the peak of the pandemic, the number of COVID-19–related communities on Weibo increased by nearly 40% compared with the prepandemic period [49,50]—a rise substantially greater than that observed on Reddit. This discrepancy may reflect differences in user engagement between the 2 platforms, as well as potential gaps in the Reddit data available for analysis. A second notable spike in SLE-related discussions on Weibo occurred from 2023 to 2024, which may be attributed to celebrity influence [51]: the death of a well-known actor from SLE may have drawn renewed public attention to the disease, possibly contributing to a wave of popular science communication and a new focal point in public discourse.

When interpreting the topic modeling results, we observed topics on both platforms containing treatment-related misinformation alongside concerns reported by people with SLE and culturally situated health beliefs. One recurring pattern was concerns reported by people with SLE about pharmacological treatment, where distressing experiences were generalized into cautionary claims (eg, “Taking HCQ for a week and just had a second acute attack of extreme pain and diarrhea and just decided it cannot go on, it just cannot, I am unable to live like this.” [Reddit topic 7]; “Taking hormones to treat lupus erythematosus causes femoral head necrosis!” [Weibo topic 74]). Alternative and complementary therapies such as the autoimmune protocol (AIP) diet and supplements, including Reishi mushrooms and brown algal astaxanthin, were occasionally framed as curative alternatives (eg, “I also take natural, ground Reishi mushroom capsules. They contain a heap of healing powers...and are very effective at healing lupus.” [Reddit topic 73]; “Lupus erythematosus...we must take brown algal astaxanthin.” [Weibo topic 175]). A few topics referenced scientifically unsupported or commercially promoted “cures” (eg, “I went to a ‘Hindu’ monk when I was 11/12, and he told me I could be cured if I drank my own urine every morning for a year.” [Reddit topic 80]; “The China International Academy of Medicine has invented a drug that can cure lupus erythematosus... [Skin No. 1]...” [Weibo topic 117]). Polarized disease perceptions and pessimistic narratives likely reflected emotional responses to chronic illness rather than misinformation in a strict sense (eg, “She had lupus erythematosus, which was more serious than cancer...” [Weibo topic 108]; “I knew that I had lost the right to live a normal life…” [Weibo topic 1]). Some posts also reflected distrust of physicians and a stronger inclination to trust peer experiences, potentially facilitating the circulation of unverified content (eg, "You can’t really speak to every doctor’s intentions either. Plenty of people have been told that they are making things up, for instance.” [Reddit topic 82]). As Barahona-Correa et al [36] observed, some individuals use social media platforms as their default search engines, and the easy access to unverified information makes these platforms a potent channel through which misconceptions may influence public perception and treatment decisions.

We did not quantify the prevalence of these content types or formally distinguish misinformation from legitimate patient experiences, so these cross-platform similarities should be interpreted as exploratory observations rather than empirically validated patterns. Nevertheless, their recurrence across both Reddit and Weibo suggests that treatment-related misconceptions and unverified remedies may pose shared challenges in online communities of people with SLE, warranting dedicated studies with systematic misinformation identification protocols grounded in clinical guidelines.

This study presents several notable strengths. First, unlike traditional public opinion assessments that often rely on Gallup-style self-reported surveys, our research incorporates internet platforms into the measurement framework, thereby extending the methodological boundaries of information systems [52]. Internet-based data can serve as an early warning signal for public health and contribute to the detection and correction of health-related misinformation [53,54]. Simultaneously, these data provide access to authentic concerns and lived experiences reported by people with SLE—insights that are difficult to capture through traditional channels—thus offering valuable evidence to improve medical communication and optimize clinical workflows [55]. Furthermore, emotional support and information sharing within online communities may enhance patient empowerment and satisfaction. Importantly, because these data are generated in natural, noninterventional contexts, the risk of observation bias is minimized.

Another strength of this study lies in the parallel comparison of discussions across 2 major social media platforms, Reddit and Weibo. In contrast to studies that focus on a single platform, our cross-platform and cross-cultural design not only reveals differences in information diffusion and disease perception but also highlights platform-specific discourse patterns. This approach broadens the comparative scope of our findings and provides valuable insights for addressing the current research gap in public health communication across cultural settings [56,57].

Several limitations should be acknowledged. First, because the abbreviation “SLE” was excluded from the Weibo search terms, Weibo discussions that used this abbreviation rather than the full Chinese disease terms may have been missed, which may have led to an underestimation of the Weibo corpus and a slight underrepresentation of users who prefer shorthand expressions. Second, as SLE disproportionately affects women—with reported female-to-male ratios ranging from 1.2:1 to 15:1 [58,59]—and women are generally more active on social media [60], the absence of demographic information such as gender limited our ability to adjust for sample structure or examine gender-related differences in discussion activity. Third, the unreviewed nature of social media content introduces substantial volumes of false or misleading information (eg, COVID-19 vaccine–related rumors), complicating data cleaning and potentially affecting the reliability of conclusions [61]. Fourth, social media users constitute a self-selected subgroup that likely differs systematically from the broader population of people with SLE: individuals with greater disease burden, stronger emotional needs, or higher health literacy may be more inclined to contribute to online discussions, while those with limited internet access, lower digital literacy, or from underrepresented groups remain largely absent. Therefore, the concerns and needs identified may not represent the full spectrum of experiences among people with SLE, and caution is warranted when generalizing the findings [62]. Fifth, a small number of highly active users contributed a disproportionately large share of content, while the voices of the majority of “silent” users remained underrepresented. This imbalance may have exaggerated the apparent engagement and influence of certain subgroups [63]. Finally, the cross-lingual design may have introduced additional bias: machine translation of Chinese content may have altered culturally specific or emotionally nuanced expressions, and although multilingual embeddings enabled joint analysis of Chinese and English texts, they may not have captured culturally embedded meanings with equal accuracy across languages [64]. Cultural differences in emotional expression may have influenced sentiment classification and thematic interpretation. Furthermore, the 2 platforms were sampled using different strategies: Reddit data were drawn from a single subreddit (r/lupus), whereas Weibo data were obtained through broader keyword-based retrieval. This difference may affect the representativeness of each corpus and the strength of cross-platform generalization.

Future research should incorporate clinical indicators, patient-reported outcomes, and registry data to validate whether social media sentiment reflects disease burden and quality of life among people with SLE, and the integration of such data into AI predictive models could enable real-time monitoring of experiences among people with SLE, prediction of disease trajectories, and the development of personalized, data-driven support strategies.

In summary, this study demonstrates the feasibility of using AI to perform topic modeling and sentiment analysis on large-scale, cross-cultural, and cross-platform social media data related to SLE. Beyond this, the distinct informational and emotional needs across platforms indicate that culturally tailored communication strategies are needed for people with SLE, while the recurrence of treatment-related misconceptions calls for clinicians and public health authorities to engage with online communities of people with SLE. More broadly, this cross-platform approach provides a transferable framework for real-time monitoring of patient perspectives beyond SLE.