Social and semantic network analysis of chat logs

Abstract

Multi-user virtual environments (MUVEs) allow many users to explore the environment and interact with other users as they learn new content and share their knowledge with others. The semi-synchronous communicative interaction within these learning environments is typically text-based Internet relay chat (IRC). IRC data is stored in the form of chatlogs and can generate a large volume of data, posing a difficulty for researchers looking to evaluate learning in the interaction by analyzing and interpreting the patterns of communication structure and related content. This paper describes procedures for the measurement and visualization of chat-based communicative interaction in MUVEs. Methods are offered for structural analysis via social networks, and content analysis via semantic networks. Measuring and visualizing social and semantic networks allows for a window into the structure of learning communities, and also provides for a large cache of analytics to explore individual learning outcomes and group interaction in any virtual interaction. A case study on a learning based MUVE, SRI's Tapped-In community, is used to elaborate analytic methods.