Clustering social networks using interaction semantics and sentics

Abstract

The passage from a static read-only Web to a dynamic read-write Web gave birth to a huge amount of online social networks with the ultimate goal of making communication easier between people with common interests. Unlike real world social networks, however, online social groups tend to form for extremely varied and multi-faceted reasons. This makes very difficult to group members of the same social network in subsets in a way that certain types of contents are shared with just certain types of friends. Moreover, such a task is usually too tedious to be performed manually and too complex to be performed automatically. In this work, we propose a new approach for automatically clustering social networks, which exploits interaction semantics and sentics, that is, the conceptual and affective information associated with the interactive behavior of online social network members.