ArticleJournal of medical Internet research2025
Exploring Inflammatory Bowel Disease Discourse on Reddit Throughout the COVID-19 Pandemic Using OpenAI's GPT-3.5 Turbo Model: Classification Model Validation and Case Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Large Language Model-Assisted Annotation Framework for Cross-Platform Analysis of Online Autism Communities: Implications for Parent Education and Digital Support.Journal of medical Internet research · 2026Article
- AI-powered Chatbot integration for enhanced accessibility of electronic health records in a pediatric hospital.Scientific reports · 2026Article
- Clinical superiority of belly-tendon montage over others for recording air-conducted ocular vestibular evoked myogenic potential.Scientific reports · 2026Article
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5 authors.
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Abstract
backgroundInflammatory bowel disease (IBD) is a chronic autoimmune disorder with an increasing prevalence in the general population. Internet-based communities have become vital for communication among patients with IBD, especially throughout the COVID-19 pandemic. However, these internet-based patient-to-patient communications remain largely underexplored.
objectiveThis study aims to analyze community posts from 3 of the largest IBD support groups on Reddit between March 1, 2020, and December 31, 2022, using a pretrained transformer model, and to validate the classification system's results via comparison to human scoring.
methodsWe collected posts (N=53,333) from subreddits r/CrohnsDisease, r/UlcerativeColitis, and r/IBD and classified them using OpenAI's GPT-3.5 Turbo model to determine sentiment, categorize topics, and identify demographic information and mentions of the COVID-19 pandemic. A subset of posts (n=397) was manually scored to measure interrater agreement between human raters and the GPT-3.5 Turbo model.
resultsFleiss κ and Gwet AC1 coefficients indicated a high level of agreement between raters, with values ranging from 0.53 to 0.91. The raters demonstrated almost perfect agreement on the classification of gender, with a Fleiss κ of 0.91 (P<.001). Medications (14,909/53,333) and symptoms (14,939/53,333) emerged as the most discussed topics, and most posts conveyed a neutral sentiment. While most users did not disclose their age, those who did primarily belonged to the 20-29 years (2392/4828) and 30-39 years (859/4828) age groups. Based on self-reported gender, we identified 1509 men and 1502 women among our IBD Reddit users. When comparing the users on the IBD subreddits to the general IBD population, there was a significant difference in gender distribution (N=3,090,011; χ
conclusionsOur study showcases the potential of generative pretrained transformer models in processing and extracting insights from medical social media data. Future research can benefit from further subanalyses of our validated dataset or use OpenAI's model to analyze social media data for other conditions, particularly those for which patient experiences are challenging to collect.
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