Analysis of communities in social media

Abstract

Social media have already woven themselves into the very fabric of everyday life. There are a variety of applications and associated computational social systems. Furthermore, we observe the emergence into more mobile and ubiquitous applications. Various social applications provide for a broad range of user interaction and communication. In this setting, data mining and analysis plays a central role, e.g., for automatically detecting associations and relationships, and identifying interesting topics. In particular, in this talk I will consider the discovery and analysis of communities, e.g., concerning users and user-generated content. Such communities can be applied, for example, for personalization or generating recommendations. However, while there exists a range of community mining options, a thorough evaluation and assessment typically relies on existing gold-standard data or costly user-studies. This talk presents approaches for the analysis of communities and descriptive patterns in social media. Methods for mining and assessing communities and descriptive patterns will be introduced. The proposed analysis methodology provides for a cost-efficient approach for identifying descriptive and user-interpretable communities, since the assessment is performed using secondary data that is easy to acquire. In this talk, I will provide examples for the presented analysis techniques using social data from real-world systems. In particular, I will focus on data from the social bookmarking system BibSonomy (http://www.bibsonomy.org), and from the social conference guidance system Conferator (http://www.conferator.org).