Identifying mental health discussion topic in social media community: subreddit of bipolar disorder analysis

Author:

Timakum Tatsawan,Xie Qing,Lee Soobin

Abstract

Online platforms allow individuals to connect with others, share experiences, and find communities with similar interests, providing a sense of belonging and reducing feelings of isolation. Numerous previous studies examined the content of online health communities to gain insights into the sentiments surrounding mental health conditions. However, there is a noticeable gap in the research landscape, as no study has specifically concentrated on conducting an in-depth analysis or providing a comprehensive visualization of Bipolar disorder. Therefore, this study aimed to address this gap by examining the Bipolar subreddit online community, where we collected 1,460,447 posts as plain text documents for analysis. By employing LDA topic modeling and sentiment analysis, we found that the Bipolar disorder online community on Reddit discussed various aspects of the condition, including symptoms, mood swings, diagnosis, and medication. Users shared personal experiences, challenges, and coping strategies, seeking support and connection. Discussions related to therapy and medication were prevalent, emphasizing the importance of finding suitable therapists and managing medication side effects. The online community serves as a platform for seeking help, advice, and information, highlighting the role of social support in managing bipolar disorder. This study enhances our understanding of individuals living with bipolar disorder and provides valuable insights and feedback for researchers developing mental health interventions.

Publisher

Frontiers Media SA

Subject

General Medicine

Reference37 articles.

1. Apache OpenNLP Developer Documentation.pdf. OpenNLP2011

2. “Latent dirichlet allocation based multi-document summarization,” AroraR. RavindranB. Proceedings of SIGIR 2008 Workshop on Analytics for Noisy Unstructured Text Data, AND'082008

3. Latent dirichlet allocation9931022 BleiD. M. EduB. B. NgA. Y. EduA. S. JordanM. I. EduJ. B. J. Mach. Learn. Res.32003

4. The language of positive mental health: findings from a sample of Russian Facebook users;Bogolyubova;SAGE Open,2020

5. Social media use for health purposes: systematic review;Chen;J. Med. Inter. Res.,2021

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