Social interaction mining in small group discussion using a smart meeting system

Abstract

In this paper, we propose a mining method to discover high-level semantic knowledge about human social interactions in small group discussion, such as frequent interaction patterns, the role of an individual (e.g., the "centrality" or "power"), subgroup interactions (e.g., two persons often interact with each other), and hot sessions. A smart meeting system is developed for capturing and recognizing social interactions. Interaction network in a discussion session is represented as a graph. Interaction graph mining algorithms are designed to analyze the structure of the networks and extract social interaction patterns. Preliminary results show that we can extract several interesting patterns that are useful for interpretation of human behavior in small group discussion. © 2011 Springer-Verlag.