Known by Who We Follow: A Biclustering Application to Community Detection

Abstract

The detection of communities in social networks is a task with multiple applications both in research and in sectors such as marketing and politics among others. In this paper, we address the task of detecting on-line communities of Twitter users for a given domain. Our main contribution consists in modelling the community detection problem as a biclustering task.We have performed the experimentation with data from the political domain, a very dynamic area with a large number of interested users and a high availability of tweets. We have evaluated our proposal using both extrinsic and intrinsic methods, reaching very good results in both cases. We use the silhouette coef cient as intrinsic metric for clustering evaluation, and a classi cation task of political leanings of Twitter users as extrinsic evaluation. One of the most interesting conclusions of our experiments is the quality, from the point of view of predictive capacity in the classi cation task, of the communities identi ed with the proposed method. The information provided by communities detected through ``follow'' relationships has a predictive capacity comparable to that of the contents of tweets written by users. The results also show how detected communities can give insights about future events related to these communities that arise around social networks.