Summary
Background: Detecting community structures in complex networks is a problem interesting to several
domains. In healthcare, discovering communities may enhance the quality of web offerings
for people with chronic diseases. Understanding the social dynamics and community
attachments is key to predicting and influencing interaction and information flow
to the right patients.
Objectives: The goal of the study is to empirically assess the extent to which we can infer meaningful
community structures from implicit networks of peer interaction in online healthcare
forums.
Methods: We used datasets from five online diabetes forums to design networks based on peer-interactions.
A quality function based on user interaction similarity was used to assess the quality
of the discovered communities to complement existing homophily measures.
Results: Results show that we can infer meaningful communities by observing forum interactions.
Closely similar users tended to co-appear in the top communities, suggesting the discovered
communities are intuitive. The number of years since diagnosis was a significant factor
for cohesiveness in some diabetes communities.
Conclusion: Network analysis is a tool that can be useful in studying implicit networks that
form in healthcare forums. Current analysis informs further work on predicting and
influencing interaction, information flow and user interests that could be useful
for personalizing medical social media.
Keywords
Diabetes - social networks - homophily - assortativity - community detection
