LAUSR

Virtual worlds present a natural test bed to observe and study the social behaviors of people at a large scale. Analyzing the rich “big data” generated by the activities of players in a virtual world enables us to better understand the online society, to validate and propose sociological theories, and to provide insights of how people behave in the real world. However, how to better store and analyze such complex big data has always been an issue that prevents in-depth analyses. In this paper, we first review the research on trust in virtual worlds and Semantic Web as applied in social network analysis. Then, we present how we employed Semantic Web technologies to address this issue, and how we explored certain social concepts expressed within a massively multiplayer online game—Ever Quest II. Specifically, the relations between mentors and mentees in the game are studied. We use the housing network to measure the trust between mentors and mentees and adopt the logistic regression model to identify the predictors of building trust between them. Our research sheds light on how to analyze large-scale data within a virtual world by exploring the flow of trust in different layers of social networks with the help of Semantic Web technologies.