This study aims to explore the evolutionary pattern of discussion topics over time in an online depression self-help community.
Using the Latent Dirichlet Allocation (LDA) method, the authors analyzed 17,534 posts and 138,567 comments posted over 8 years on an online depression self-help group in China and identified the major discussion topics. Based on significant changes in the frequency of posts over time, the authors identified five stages of development. Through a comparative analysis of discussion topics in the five stages, the authors identified the changes in the extent and range of topics over time. The authors discuss the influence of socio-cultural factors on depressed individuals' health information behavior.
The results illustrate an evolutionary pattern of topics in users' discussion in the online depression self-help group, including five distinct stages with a sequence of topic changes. The discussion topics of the group included self-reflection, daily record, peer diagnosis, companionship support and instrumental support. While some prominent topics were discussed frequently in each stage, some topics were short-lived.
While most prior research has ignored topic changes over time, the study takes an evolutionary perspective of online discussion topics among depressed individuals. The authors provide a nuanced account of the progression of topics through five distinct stages, showing that the community experienced a sequence of changes as it developed. Identifying this evolutionary pattern extends the scope of research on depression therapy in China and offers a deeper understanding of the support that individuals with depression seek, receive and provide online.
This study is supported by the National Social Science Fund of China (grant number 22CTQ010), the Major Projects of Philosophy and Social Science Research in Jiangsu Province Grant No. 2022SJZD094, and the Social Science Fund of Jiangsu Province Grant No. 22TQC002.
Sun, H.L. and Fichman, P. (2023), "Evolution of discussion topics on an online depression self-help group", Library Hi Tech, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/LHT-07-2022-0317
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