# From the User to the Medium: Neural Profiling Across Web Communities

## TL;DR

This paper introduces NeuroCom, a neural embedding-based method for detecting nuanced online community structures by analyzing user-generated content, improving upon existing community detection techniques.

## Contribution

NeuroCom is a novel neural profiling approach that captures semantic relations in textual data to identify diverse and nuanced online communities.

## Key findings

- NeuroCom outperforms traditional methods in clustering accuracy.
- It identifies more detailed and meaningful discussion topics.
- The approach enhances understanding of community heterogeneity.

## Abstract

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.