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. 2023 Dec 27;13(1):22979.
doi: 10.1038/s41598-023-50490-w.

Leveraging stylometry analysis to identify unique characteristics of peer support user groups in online mental health forums

Affiliations

Leveraging stylometry analysis to identify unique characteristics of peer support user groups in online mental health forums

Yong-Bin Kang et al. Sci Rep. .

Abstract

Online peer support mental health forums provide an effective and accessible form of support, augmenting scarce clinical and face-to-face assistance. However, to enhance their effectiveness, it is essential to understand the unique characteristics of peer support user groups, and how they participate, contribute and communicate in these forums. This paper proposes and tests a novel approach that leverages stylometry analysis to uncover the unique characteristics of peer support user groups in such forums. Our approach identifies how each group empowers and supports other users, and what distinguishes them from others. The analysis shows that emotion-related words are crucial in identifying and distinguishing user groups based on their writing style. Comparative analysis of emotion expressions across user groups also uncovers the significance of emotional content in these forums in promoting mental well-being. Valued 'senior contributors' were more likely than all other groups including trained community guides to use a wide range of both positive and negative emotions in their posts. These findings have significant implications for improving the training of peer-mentors and moderators, scaling forum services, and improving guidelines for emotional expression among peer support users. Our approach presents an objective approach to differentiating the characteristics and communication patterns of valued senior contributors, mentors, and guides, enabling service providers to foster the kinds of communication that supports positive outcomes for distressed users.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Comparison of the user group prediction performance using the four types of stylometric features with a range of the most frequent words in the stylometry analysis.
Figure 2
Figure 2
Comparison of the use of the 8 emotions across all 5 user groups.

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