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

Peer-to-Peer Support and Online Communities (693) Medicine 2.0: Social Media, Open, Participatory, Collaborative Medicine (1744) Information Seeking, Information Needs (515) Machine Learning (1637) Natural Language Processing (767) Respiratory Medicine (107) Design and Usability of Websites for Special User Groups (194) Demographics of Users, Social & Digital Divide (806) Consumer & Patient Education and Shared-Decision Making (743) Participatory Medicine & E-Patients (820)

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

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 Tweetations (1) Metrics
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Anna De Simoni Anna De Simoni @Anna_De_Simoni

Using deep learning, we found that #superusers in online health communities drive positive engagement #peersupport #asthma #respiratoryconditions https://t.co/n9YeinGGHZ

8:36 AM · Feb 20, 2025

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