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.
Neural representation has been implemented for social media short messages albeit in different ways than we propose @cite_1 . Gaussian Restricted Boltzmann Machines (RBM) have been used for modeling user's posts within a social network to identify their topics of interest, and finally construct communities @cite_1 . However, a parametric approach was used in which it was necessary to specify the number of clusters communities. In addition to not requiring such initialization, this approach is conceptually different than our proposed work in that it directly maps individuals to communities (instead of mapping the content of their posts, which may better capture heterogeneous community memberships). Further, content-based density approachefs as proposed, versus parametric ones could potentially learn a more organic number of communities. Given this gap, and the fact that content-based community detection (opposed to graph-based) may be more pertinent in health-related communities, here we explore content-based clustering of health communities.
@cite_1
2078525157
Online social networks have been wildly spread in recent years. They enable users to identify other users with common interests, exchange their opinions, and expertise. Discovering user communities from social networks have become one of the major challenges which help its members to interact with relevant people who have similar interests. Community detection approaches fall into two categories: the first one considers user' networks while the other utilizes usergenerated content. In this paper, a multi-layer community detection model based on identifying topics of interest from user published content is presented. This model applies Gaussian Restricted Boltzmann Machine for modeling user's posts within a social network which yields to identify their topics of interest, and finally construct communities. The effectiveness of the proposed multi-layer model is measured using KL divergence which measures similarity between users of the same community. Experiments on the real Twitter dataset show that the proposed deep model outperforms traditional community detection models that directly maps users into corresponding communities using several baseline techniques.