Community discovery and profiling with social messages

Abstract

Discovering communities from social media and collaboration systems has been of great interest in recent years. Existing work show prospects of modeling contents and social links, aiming at discovering social communities, whose definition varies by application. We believe that a community depends not only on the group of people who actively participate, but also the topics they communicate about or collaborate on. This is especially true for workplace email communications. Within an organization, it is not uncommon that employees multifunction, and groups of employees collaborate on multiple projects at the same time. In this paper, we aim to automatically discovering and profiling users' communities by taking into account both the contacts and the topics. More specifically, we propose a community profiling model called COCOMP, where the communities labels are latent, and each social document corresponds to an information sharing activity among the most probable community members regarding the most relevant community issues. Experiment results on several social communication datasets, including emails and Twitter messages, demonstrate that the model can discover users' communities effectively, and provide concrete semantics. © 2012 ACM.