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Community Structure of a Mental Health Internet Support Group: Modularity in User Thread Participation.

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Summary

User registration date significantly shapes online mental health communities. Early registration indicates more active and central users, forming distinct community generations on platforms like BlueBoard.

Keywords:
internetmental healthmodularitysocial networksuper usersupport group

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Area of Science:

  • * Social Network Analysis
  • * Online Community Structure
  • * Mental Health Support

Background:

  • * Limited quantitative data exists on mental health internet support group community structures.
  • * Understanding user interaction factors is crucial for service design and future research.

Purpose of the Study:

  • * To determine user characteristics associated with subgroup community structure in a mental health internet support group.
  • * To identify predictors of community modularity and subgroup formation.

Main Methods:

  • * Social network analysis using the Louvain method on the BlueBoard internet support group.
  • * Assessed demographic characteristics (age, gender, location, user type) and posting frequency.
  • * Analyzed registration date as a predictor of subgroup structure via multinomial logistic regression.

Main Results:

  • * Identified five distinct subgroups within the BlueBoard community.
  • * Registration date was the most significant predictor of community modularity, improving classification accuracy by 46.3%.
  • * Users with earlier registration dates were more active and central within their subgroups.

Conclusions:

  • * The five subgroups represent distinct 'generations' of the BlueBoard community, transcending specific mental health topics.
  • * Highly engaged and central users, identified by early registration, likely influence community formation and structure.
  • * Further research is needed to confirm generalizability and explore the role of active users in phenomenon formation.