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
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In this paper we present an original approach for community detection in complex networks. The approach belongs to the family of seed-centric algorithms. However, instead of expanding communities around selected seeds as most of existing approaches do, we explore here applying an ensemble clustering approach to different network partitions derived from ego-centered communities computed for each selected seed. Ego-centered communities are themselves computed applying a recently proposed ensemble ranking based approach that allow to efficiently combine various local modularities used to guide a greedy optimization process. Results of first experiments on real world networks for which a ground truth decomposition into communities are known, argue for the validity of our approach.