From the User to the Medium: Neural Profiling Across Web Communities
This work addresses the need for better community detection in online health and support groups, though it is incremental as it builds on existing neural methods for text analysis.
The paper tackles the problem of identifying nuanced discussion topics and user communities in diverse online groups by developing NeuroCom, a method that uses neural representations of user content to find dense user groups in a latent space, showing improved clustering over existing unsupervised approaches.
Online communities provide a unique way for individuals to access information from those in similar circumstances, which can be critical for health conditions that require daily and personalized management. As these groups and topics often arise organically, identifying the types of topics discussed is necessary to understand their needs. As well, these communities and people in them can be quite diverse, and existing community detection methods have not been extended towards evaluating these heterogeneities. This has been limited as community detection methodologies have not focused on community detection based on semantic relations between textual features of the user-generated content. Thus here we develop an approach, NeuroCom, that optimally finds dense groups of users as communities in a latent space inferred by neural representation of published contents of users. By embedding of words and messages, we show that NeuroCom demonstrates improved clustering and identifies more nuanced discussion topics in contrast to other common unsupervised learning approaches.