The methodology followed to identify the RMDs-related subreddits is similar to previously described methods.18 Briefly, five main steps were taken:

• 1.

  Search strategy: The Reddit search engine, with the ‘Community’ filter enabled, was used. To ensure thoroughness, a pre-compiled list of terms commonly associated with rheumatology was used to guide the search: arthritis, autoimmune, back pain, behçet, chondropathies, connective tissue, fibromyalgia, gout, lupus, myositis, osteopathy, psoriasis, Raynaud, rheumatology, scleroderma, Sjogren, spondylitis, tendinitis, thritis, uveitis, vasculitis. Initially, all subreddits that could be related, to some extent, to RMDs were manually annotated.

• 2.

  Metadata extraction: For each potential subreddit identified, we collected the following information: disease; community name; creation date; public, private, or restricted status; community description, number of subscribers (as of 14 May 2024) and last year's activity (i.e., at least one new post).

• 3.

  Relevance classification: After that, a rheumatologist manually reviewed the subreddits and classified them according to their relevance in the RMDs field into three categories: no affinity, low affinity, or high affinity.

• 4.

  Eligibility screening: Only subreddits with over 1000 subscribers that were active between May 2023 and May 2024 (i.e., hosting at least one new post), with a strong affinity, and retrievable from Pushshift.io were initially considered for further analysis. If multiple subreddits associated with the same pathology remained, we either kept both or selected just one for inclusion (i.e., the largest community).

• 5.

  Data retrieval verification: Finally, to ensure a diverse sample of autoimmune diseases, we relaxed the requirement that data be sourced exclusively from the Pushshift.io torrent. In cases where a subreddit was not retrievable from Pushshift.io, a manual request was made to the Pushshift.io Reddit administrator to obtain such data.

The data used in this study were obtained exclusively via a publicly available torrent. More details about Pushshift.io can be found in Supplementary Material “Pushshift.io” section.

Different metrics and statistics (e.g., number of users, messages, comments; thread length, word count per submission) were computed to characterise the different subreddits. To ensure the accuracy of distinct metrics while preserving the integrity of total activity data, we applied a stratified preprocessing approach:

• 1.

  Platform-level volume metrics: For aggregate statistics (e.g., total submissions, activity over time), no filtration was applied. Entries marked as [deleted] or [removed] in the body field, those containing one character or fewer, and automated messages (e.g., starting with “Welcome to r/”) were included. This ensures that the metadata representing the total traffic load on the community was preserved.

• 2.

  User-level metrics: For statistics describing user behaviour (e.g., messages per user), records where the author field appeared as [deleted] were excluded. This filter was applied to prevent the aggregation of multiple distinct users who had deleted their accounts into a single “deleted” entity, which would otherwise introduce significant bias into user participation distribution.

• 3.

  Engagement ratios: When calculating engagement metrics (e.g., number of comments on the top ten most commented posts), comments missing a linkage to a parent submission were excluded to prevent the skewing of ratio calculations.

Continuous variables were summarised using the median and the first and third quartiles [Q1–Q3]. The full list of metrics and statistics is shown in the Supplementary Material “Previous use of Reddit in rheumatology” section. Finally, correlations between subreddits characteristics were analysed using Spearman correlation, adjusting p-values using the false discovery rate.

To categorise discussion posts effectively, we manually identified recurring themes across posts and established a set of relevant categories based on content, user intent, and emotional tone. Each post was then classified into one or more of these predefined categories.

To assess the emotional tone across the selected communities, a sentiment analysis was performed on all submissions from the 20 selected subreddits. A pre-trained RoBERTa-based language model, cardiffnlp/twitter-roberta-base-sentiment-latest, fine-tuned with social media data from Twitter was used to classify each submission as negative, neutral, or positive.19 Briefly, this RoBERTa-based model evaluates sentiment by utilising a deep neural network architecture that has been pre-trained on a vast collection of texts to learn the contextual relationships between words. By fine-tuning the model with labelled Twitter sentiment data, it becomes adept at analysing text inputs, capturing both the semantic meaning and subtle contextual nuances, thereby facilitating accurate classification of sentiment.

The submissions were processed as follows: URLs, and title tags were removed, and submissions without a title or body were excluded (i.e., [deleted] or [removed]). Finally, the title and the body of each submission were combined into a single record and analysed. Comments on those submissions from other users were not considered. We did not apply topic filters beyond subreddit membership; therefore, posts may occasionally include community-building or off-topic content. Sentiment was computed at the post level, independent of topic.

R version 4.3.2 was used for the descriptive analyses, while Python 3.11 was employed for the sentiment and topic modelling analyses.

The reporting of this study conforms to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement.20

The pre-compiled list of terms allowed us to identify 83 subreddits or communities potentially related to RMDs (Table 1). Notably, over 70 of them were classified as having some degree of relevance in the RMDs field. Supplementary Excel File Subreddits shows the general information of the 83 subreddits identified, including their fulfilment of the eligibility criteria. After applying these criteria, 20 communities were selected for further analysis (Fig. 1 presents the eligibility criteria diagram).

Fig. 1.

Inclusion criteria diagram.

Table 2 presents the main characteristics of the 20 selected subreddits. The oldest identified community is r/Fibromyalgia, which dates back to 2009, whereas the newest is r/rheumatoidarthritis, created in 2018. Regarding the number of subscribers, the community with the highest number was r/Fibromyalgia, and the lowest was r/Behcets. We observed a non-significant correlation between the lifetime of these communities and the number of subscribers (rho=0.69, p=0.095). Furthermore, when studying annual. User participation in each subreddit (Supplementary Figs. 1 and 2), a common characteristic was observed: from 2016 to 2017 onward, growth typically followed an exponential pattern.

Regarding the subreddit content, we observed substantial heterogeneity across communities in the cumulative number of submissions and comments, the median daily activity rate (i.e., number of messages, including submissions and comments, posted per day), and the level of engagement. On the one hand, we observed that both the cumulative number of submissions and comments were positively correlated with the number of subscribers (rho=0.97, p<0.0001; rho=0.92, p<0.0001, respectively). Furthermore, the median number of messages per day was also correlated with the number of subscribers (rho=0.74, p=0.028), as well as with the cumulative number of both submissions and comments (rho=0.81, p=0.002; rho=0.86, p=0.0002, respectively). On the other hand, regarding engagement, the comment-to-submission ratio showed only a significant correlation with the median number of messages per user (rho=0.86, p=0.0002).

Peak user interaction typically occurred between 16:00 and 23:00, as shown in Supplementary Figs. 3 and 4. The days of the week exhibiting the highest levels of activity were Thursdays, Wednesdays, Fridays, and Tuesdays, with 9, 7, 2, and 2 communities, respectively (Supplementary Figs. 5 and 6). For all but one of the communities analysed, r/autoimmunity, the majority of activity was concentrated in the second half of the year, with December (8 communities), October (4 communities), and August (3 communities) experiencing the highest levels of engagement (Supplementary Figs. 7 and 8). We observed no significant correlations between these times of peak activity (hour, weekday or month) and the other studied characteristics nor among themselves.

Finally, the titles and bodies of the ten most-commented threads per subreddit were collected (Supplementary Excel File Most Commented Threads, “posts” sheet). In Table 3, the titles of the threads with the highest number of comments per subreddit are shown. After manual review of titles and bodies, 32 different themes or categories were identified (Supplementary Excel File Most Commented Threads_R1, “categories” sheet). A brief definition of each category is also included. We observed that 107, 62, 26, and 5 threads were characterised using 1, 2, 3 or 4 themes, respectively.

When analysing the themes’ characteristics (Supplementary Excel File Most Commented Threads “categories_analysis” sheet), we observed that “Patients Like Me”, “Asking for Emotional Support”, and “Asking for Demographic Information” were the three most frequently identified. In addition, they appeared across the highest number of communities. Furthermore, a significant number of the identified themes (8 out of 32, 25%) appeared across ten or more communities. Finally, themes present in posts with the highest number of comments were those related to “Asking for Impact of Environment/Life Events on Disease”, “Ask Me Anything (AMA)” and “Sharing Working Experience”.

The top five most positive and negative comments from each community were extracted and can be seen in Supplementary Excel File Sentiment Analysis. Moreover, Fig. 2 depicts the prevalence of negative, neutral, and positive classes across all analysed submissions in each subreddit. The highest percentage of negative comments pertained to musculoskeletal disorders of mechanical origin, such as costochondritis or back pain, as well as to fibromyalgia. r/Fibromyalgia is the most polarised community with the fewest neutral comments. The sentiment analysis conducted revealed the emotional experiences of individuals with RMDs. These posts frequently described intense physical pain, emotional distress, and frustration related to the progression of chronic conditions. Phrases such as “cripples me under a load of severe pain” and “I feel so worthless” illustrate the profound impact that chronic illness can have on quality of life. Users also expressed dissatisfaction with medical treatments, highlighting the perceived inefficacy of certain interventions, delayed diagnoses, or mismanagement by healthcare professionals (e.g., “I hate doctors. My rheumatologist gaslit me and doesn’t think my pain is real. Said she thinks it's just the area overly sensitised. Waited 6 months to speak with her and this bullshit happened”). Moreover, a recurring sentiment across multiple subreddits was frustration with the healthcare system (e.g., “USA have the worst healthcare in the world”). Many users expressed dissatisfaction with long wait times, misdiagnoses, financial burdens (e.g., “Cannot afford the endless drugs and tests and don’t know if I’ll be able to work when I finally find work”), and lack of access to effective treatments. Finally, chronic pain and its impact on daily life and mental health were recurring topics, with users expressing how debilitating these conditions can be.

Fig. 2.

Sentiment in RMDs Reddit communities sorted by negative percentage.

Despite the overall dominance of negative sentiment, positive sentiments were present, particularly in posts where users reported successful treatments, improvements in symptoms, or expressed gratitude for community support. These posts were often marked by expressions of relief and optimism. For example, posts like “Cosentyx is working!!!” and “My doctor signed off for permanent disability placard!!!:) it's a good day everybody”.

Users frequently expressed appreciation for the support and advice shared within these online spaces. Positive posts often referenced the sense of belonging and relief that came from discussing their experiences with others facing similar challenges (e.g., “One week to go until I run 50 miles. This community has been amazing, and thank you all for the support!”).

Finally, sentiment analysis was combined with the thematic characterisation of the ten most-commented posts in each community. The aggregation of positive/negative/neutral probabilities by category allowed us to identify dominant emotional trends within each theme. The categories associated with the highest negative sentiments were those related to “Expressing Hopelessness”, “Asking for Help: Symptom Management”, and “Lack of Understanding from Healthy Peers”. Conversely, those themes exhibiting greater neutral sentiments were “Asking for Demographic Information”, “Asking for Experiences with Medical Procedures”, and “Asking for Causes of Disease”. No theme showed predominant positive sentiments.

• •

  It should be noted that the information extracted from Reddit is not representative of the general population suffering from musculoskeletal diseases. Patients with RMDs are usually elderly people and women. Conversely, Reddit users tend to be male between 18 and 34 years old, as highlighted by Proferes et al.26 Furthermore, given Reddit's predominance of English-language content, our analysis inherently excludes patients with no knowledge of English. Other existing challenges in the use of Reddit include privacy concerns, self-reported inaccuracies, misinformation, low data quality, limited content reliability, and ethical aspects. Fiesler et al.27 have recently described ethical considerations in Reddit research.

• •

  The use of Pushshift.io to obtain Reddit data is not without limitations. First, not all subreddits are accessible through Pushshift. Second, because the data are not collected in real time, some posts and comments may be missing or deleted. Third, uncompressing and processing the large data files can be technically challenging. Beyond these practical issues, there are also ethical and legal considerations. Pushshift may retain metadata from posts and comments that have subsequently been deleted on Reddit, which can restrict users’ ability to exercise their “right to erasure” under the GDPR. In this study, we did not attempt to recover deleted content and did not analyze personal information. To minimize re-identification risks, no usernames were collected or reported, and all verbatim excerpts were carefully screened by two authors for potential re-identifying information (including usernames, names, specific locations or institutions, exact dates, or highly idiosyncratic personal details). As a result, no paragraph was modified. While our approach is consistent with prior peer-reviewed work using Pushshift, we acknowledge that recent disputes over its compliance with Reddit's API policies raise concerns about the long-term sustainability and legitimacy of this data source.

• •

  Our study focused on subreddits with at least 1000 subscribers. While this approach helped us characterise well-established and active communities, it may also exclude smaller, potentially emerging subreddits that represent rarer or niche aspects of RMDs. This threshold was a pragmatic choice to ensure study feasibility and the robustness of our analyses, as communities with very low activity can yield unreliable conclusions and present data acquisition challenges. Consequently, several subreddits for less common RMDs, such as r/WegenersGPA, r/Myositis, and r/Vasculitis, were not included in the final analysis. We did not conduct a sensitivity analysis including subreddits with fewer than 1000 subscribers, so the impact of excluding these smaller but potentially important communities could not be quantified and should be explored in future work. However, by identifying these smaller communities in the Supplementary Material, we provide a foundation for future targeted studies that could explore the unique dynamics of these valuable, niche patient populations.

• •

  The classification of subreddits regarding their relevance to RMDs was performed by a single rheumatologist without inter-rater reliability assessment. While we consider the explicit nature of community descriptions to minimise ambiguity, we acknowledge the potential for selection bias due to the lack of a second reviewer.

• •

  A key limitation is the external validity of our sentiment analysis, as the RoBERTa-based model was fine-tuned on Twitter data. While both are social media platforms, their linguistic characteristics, user context, and community cultures differ significantly. Twitter's character limits historically encouraged brevity, abbreviations, and a faster pace of communication. In contrast, Reddit posts are often long-form, narrative, and highly contextualised within the specific culture and norms of a subreddit. This mismatch means that nuances specific to Reddit's conversational style such as sarcasm, complex narratives of patient experiences, or community-specific jargon might not be captured as effectively by a model trained on Twitter's distinct discourse patterns. Consequently, the accuracy of sentiment classification may be affected, and this should be considered when interpreting the sentiment results. To reduce the impact of this limitation we examined the generalisability of the sentiment model by evaluating it against a labelled Reddit-specific dataset as described in the Supplementary Material.

• •

  We did not perform a formal, in-domain evaluation of the sentiment classifier using manually labelled Reddit data. Specifically, we did not obtain a stratified sample of posts from rheumatology subreddits annotated by multiple human coders to quantify inter-annotator agreement and model performance. Such an evaluation would provide a more precise estimate of how well a Twitter-trained model generalises to medical Reddit.

• •

  Poorly structured texts and nuanced language, such as sarcasm, idiomatic expressions, or culturally specific references commonly found on social media, can lead to misclassification of sentiment. Moreover, while positive, negative, and neutral classes are commonly used in sentiment analysis, they often fail to capture the full spectrum of emotions and the varying intensities with which individuals may express them.

• •

  When analysing periods of heightened activity, it is important to consider that Reddit is an international platform, with users distributed across various time zones.

• •

  There are other metrics that could have been considered for the descriptive analysis, such as the number of upvotes per message, or the presence of multimedia content embedded in the messages.

• •

  We did not account for bots and automated messages, which could have artificially inflated the message count.