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This paper is in the following e-collection/theme issue:

Peer-to-Peer Support and Online Communities (990) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (2652) Participatory Medicine & E-Patients (979) Consumer & Patient Education and Shared-Decision Making (1257) Demographics of Users, Social & Digital Divide (1272) Respiratory Medicine (207) Natural Language Processing (1242) Information Seeking, Information Needs (648) Design and Usability of Websites for Special User Groups (290) Machine Learning (3082)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56038, first published .
Elderly woman smiling at a computer screen displaying a heart and the word HEALTH

Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning–Based Sentiment Analysis

Understanding the Engagement and Interaction of Superusers and Regular Users in UK Respiratory Online Health Communities: Deep Learning–Based Sentiment Analysis

Authors of this article:

Xiancheng Li1 Author Orcid Image ;   Emanuela Vaghi2 Author Orcid Image ;   Gabriella Pasi2 Author Orcid Image ;   Neil S Coulson3 Author Orcid Image ;   Anna De Simoni4 Author Orcid Image ;   Marco Viviani2 Author Orcid Image ;   AD HOC Group5
Article Authors Cited by (1) Tweetations (1) Metrics

Journals

  1. Xu Y, Ma J, Hu Y, Liu Y, Chen Y, Feng W, Zhang C, Zhang L, Zhang X, Huang R. 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 2026;28:e85290 View