Enhancing Community Discovery and Characterization in VCoP Using Topic Models

The identification of communities in social networks is a common problem that researchers have been dealing using network analysis properties. However, in environments where community members are connected by digital documents, most researchers have either emphasize to solve the community discovery problem computing structural properties of networks, ignoring the underlying semantic information from digital documents. In this paper, we propose a novel approach to combine traditional network analysis methods for community detection with text mining techniques. This way, extracted communities can be labeled according to latent semantic information within documents, called topics. Our proposal was evaluated in Plexilandia, a virtual community of practice with more than 2,500 members and 9 years of commentaries.