ORIGINAL RESEARCH article
Front. Psychiatry
Sec. Digital Mental Health
Volume 16 - 2025 | doi: 10.3389/fpsyt.2025.1503427
This article is part of the Research TopicApplication of chatbot Natural Language Processing models to psychotherapy and behavioral mood healthView all 7 articles
Identifying Yalom's Group Therapeutic Factors in Anonymous Mental Health Discussions on Reddit: A Mixed-Methods Analysis Using Large Language Models, Topic Modeling and Human Supervision
Provisionally accepted
- 1Charité University Medicine Berlin, Berlin, Germany
- 2Department of Geriatrics and Medical Gerontology, Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität Zu Berlin, Berlin, Germany, Berlin, Germany
- 3Department of Psychology, Osnabrück University, Osnabrück, Germany, Osnabrück, Germany
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Online communities provide valuable, peer-led spaces for discussing mental health issues, offering support that can complement traditional therapy. In this study, we adopt an interpretive approach by applying Yalom's group therapeutic factors to explore how mental health-focused Reddit discussions may reflect group therapy processes. We also propose a practical methodological framework for large-scale qualitative research. Using a mixed-methods approach, we integrate advanced Natural Language Processing (NLP) techniques-including Large Language Models (GPT-3.5 Turbo 16k), cosine similarity, and BERTopic-with human validation to analyze 6,745 comments from mental health-focused Subreddits. The results show that a large portion of the data can be interpreted through Yalom's therapeutic factors, such as Instillation of Hope, Group Cohesion, and Altruism, suggesting a generally supportive and empathetic online environment. However, unfiltered negative dynamics, including shared suffering and maladaptive coping strategies, also appeared in the discussions. By grounding NLP-based analyses in a well-established therapeutic framework and incorporating human expertise, we demonstrate a transparent, scalable approach to examining 1 large-scale online mental health data. These findings underscore the potential of online communities for enhancing peer-led mental health support, while emphasizing the importance of theoretical grounding in interpreting such digital spaces.
Keywords: Large language models, Thematic analysis, Topic Modeling, Mental Health, Group Therapy, peer support, online communities
Received: 18 Oct 2024; Accepted: 28 Apr 2025.
Copyright: © 2025 Ferizaj, Lalk, Lahmann and Rubel. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Drin Ferizaj, Charité University Medicine Berlin, Berlin, Germany
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