{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T08:07:41Z","timestamp":1784275661912,"version":"3.55.0"},"reference-count":32,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T00:00:00Z","timestamp":1775865600000},"content-version":"vor","delay-in-days":100,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,1,17]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Myositis is a rare autoimmune condition associated with muscle weakness, systemic involvement, and long-term disability. People living with rare conditions frequently use online platforms to share experiences and seek information beyond formal healthcare settings. This exploratory study applied automated natural language processing (NLP) methods to analyse public posts and comments from the myositis subreddit between 2020 and 2024. The dataset comprised 1223 unique posts and 18\u2009453 unique comments contributed by 3987 users. Seven sentiment models and one ensemble were evaluated, with RoBERTa-based transformer models selected for contextual analysis. Topic modeling was conducted using Latent Dirichlet Allocation and BERTopic. While BERTopic initially generated highly granular clusters, hierarchical merging produced stable, clinically interpretable themes. Six dominant discussion themes were identified: (i) autoimmune diagnosis and systemic symptoms; (ii) organ-specific concerns; (iii) fatigue, mobility, and sleep; (iv) navigating care pathways; (v) life impact; and (vi) coping strategies, lifestyle changes, and anxiety. Topic-linked sentiment analysis showed predominantly neutral sentiment overall. More negative sentiment was associated with unresolved symptoms and diagnostic uncertainty, and sentiment became increasingly negative at deeper comment levels, suggesting escalation toward unmet needs. Highly active users mainly contributed neutral, informational content. Temporal analysis identified episodic increases in engagement, including a peak in mid-2024 linked to discussions involving dermatomyositis, COVID-19, and treatment decisions. These findings demonstrate the feasibility of scalable NLP analysis of rare-disease online discourse and highlight how topic-linked emotional signals may indicate periods of uncertainty or emerging concerns.<\/jats:p>","DOI":"10.1093\/oodh\/oqag007","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T11:40:58Z","timestamp":1775821258000},"source":"Crossref","is-referenced-by-count":0,"title":["Reddit and rare diseases: what myositis communities tell us about support and struggle"],"prefix":"10.1093","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7064-6681","authenticated-orcid":false,"given":"Andrew S","family":"Wilson","sequence":"first","affiliation":[{"name":"Department of Computer Science, Birmingham City University , STEAMHouse, Belmont Row, Birmingham B4 7RQ ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamza","family":"Bin Sajid","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Birmingham City University , STEAMHouse, Belmont Row, Birmingham B4 7RQ ,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2753-2990","authenticated-orcid":false,"given":"Latika","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Rheumatology, Royal Wolverhampton Hospitals National Health Service Trust , Wolverhampton WV10 0QP,","place":["United Kingdom"]},{"name":"School of Infection, Inflammation and Immunology, College of Medicine and Health, University of Birmingham B15 2TT,","place":["United Kingdom"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,4,11]]},"reference":[{"key":"2026071703524316200_ref4","doi-asserted-by":"publisher","first-page":"51","DOI":"10.4103\/sjmms.sjmms_262_20","article-title":"Social media use for health 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