WHAT DO USERS OF RHEUMATOID ARTHRITIS ONLINE FORUMS TALK ABOUT? APPLYING A DEEP LEARNING APPROACH TO UNCOVER COMMON THEMES

ABSTRACT

BackgroundSocial media platforms have become a vital resource for individuals seeking information and support regarding health issues, including rheumatoid arthritis (RA). As such, the content generated on these platforms represents a valuable source of data for gaining insight into patients' perspectives on RA. However, previous research in this area has primarily relied on qualitative analyses of small sample sizes, limiting the ability to extract meaningful insights from social media content related to RA. With the advancement of machine learning techniques, it is now possible to analyze and extract insights from large volumes of social media posts related to RA.ObjectivesThe purpose of this study was to identify the most common topics discussed in a large dataset of submissions about RA on Reddit, one of the world's largest online forums.MethodsThe data for this study was collected from the two largest Reddit forums ("subreddits”) dedicated to RA, r/rheumatoid arthritis and r/rheumatoid, which have 18.9k and 7.6k members respectively. We retrieved all submissions but excluded responses in our analyses. All deleted or duplicate submissions and those with fewer than 10 words were removed, retaining 11,094 submissions from over 5,000 users for the analysis. To identify common themes, we applied topic modeling, a technique in natural language processing that identifies underlying themes or topics in a collection of documents. We used the Bertopic Python package (Grootendorst, 2022), which employs deep learning techniques to perform the topic modeling.ResultsThe data indicates a significant increase in submissions to the two subreddits, rising from 113 in 2014 to 2892 in 2021 and 1928 in the first 8 months of 2022. Upon analysis, 65 topics were identified, with 4162 submissions (37.5%) remaining unclassified. A topic specifically dedicated to requests to participate in surveys was removed as it did not pertain to the experiences of forum users. Among the remaining topics, the top 10 accounted for 44.90% of all submissions. To better understand each topic, a sample of 10 submissions with the highest probability for that topic were examined (Table 1).Table 1.Top 10 most frequent topicsTopicn of submissionsShare of total*Side effects of methotrexate5268.02%COVID & vaccines4627.04%Mental health4386.68%RF and anti CCP test results3315.04%RA of friends, partners, and close relatives2623.99%Complaints about rheumatologist2123.23%Questions about Humira1882.87%Questions about prednisone1822.77%Diets and RA1752.67%Early symptoms of possible RA1702.59%Exercise and RA1682.56%* After excluding unclassified topicsThree of the ten topics pertained to specific medications - methotrexate, Humira, and prednisone, accounting for 12.71% of the total. The most prevalent topic, at 8.02%, focused on the side effects of methotrexate, with many submissions inquiring about symptoms such as nausea. The second most common topic, at 7.04%, primarily revolved around COVID-19 and related issues, with some pre-COVID vaccine discussions also included. In 2021, COVID-related discussions were the most prevalent topic. The third most frequent topic (6.68% of total), dealt with mental health and the emotional struggles faced by those living with RA.ConclusionThe surge in submissions on Reddit demonstrates its growing popularity as an online forum for discussing topics related to RA. Utilizing deep learning-based topic modeling has proven to be an effective method for extracting meaningful topics from the questions and experiences shared by users. The vast amount of data generated by Reddit, in combination with advanced machine learning techniques, enables both an overview of the various topics discussed and a detailed examination of specific topics. This makes the use of social media data a valuable source of insight into the concerns of RA platform users.Reference[1]Grootendorst, M. (2022). BERTopic Neural topic modeling with a class-based TF-IDF procedure. arXiv preprint arXiv2203.05794.AcknowledgementsNIL.Disclosure of InterestsNone Decla ed.