Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56038, first published .
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

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

Xiancheng Li   1 * , PhD ;   Emanuela Vaghi   2 * , MD ;   Gabriella Pasi   2 , PhD ;   Neil S Coulson   3 , PhD ;   Anna De Simoni   4 * , PhD ;   Marco Viviani   2 * , PhD ;   AD HOC Group   5

1 School of Business and Management, Queen Mary University of London, London, United Kingdom

2 Department of Informatics, Systems and Communication, University of Milano-Bicocca, Milan, Italy

3 School of Medicine, University of Nottingham, Nottingham, United Kingdom

4 Wolfson Institute of Population Health, Asthma UK Centre for Applied Research, Queen Mary University of London, London, United Kingdom

5 See Acknowledgments, London, United Kingdom

*these authors contributed equally

Corresponding Author:

  • Xiancheng Li, PhD
  • School of Business and Management
  • Queen Mary University of London
  • Mile End Road, Bethnal Green
  • London, E14NS
  • United Kingdom
  • Phone: 44 2078825555
  • Email: x.l.li@qmul.ac.uk