Humans are adept social animals capable of identifying friendship groups from a combination of linguistic cues and social network patterns. But what is more important, the content of what people say or their history of social interactions? Moreover, is it possible to identify whether people are part of a group with changing membership merely from general network properties, such as measures of centrality and latent communities? In this paper, we address the problem of identifying social groups from conversation data and present results of an empirical study on identifying groups in a virtual world. Virtual worlds are interesting because group membership is more shaped by common interests and less influenced by cultural and socio-economic factors. Our finding is that a combination of network measures is more predictive of group membership than language cues, and that both types of features can be combined to improve prediction.

In this study, we examine human social interactions within virtual worlds and address the question of how group interactions are affected by the game environment. To investigate this problem, we introduced a set of conversational agents into the social environment of Second Life, a massively multi-player online environment that allows users to construct and inhabit their own 3D world. Our agents were created to be sufficiently lifelike to casual observers, so as not to perturb neighboring social interactions. Using our partitioning algorithm, we separated continuous public chat logs from each region into separate conversations which were used to construct a social network of the participants. Unlike many groups formed in communities and workplaces, groups in Second Life can be rapidly-forming (arising from few interactions), persistent (remaining stable over a long period), and are less affected by socio-cultural influences. In this paper, we analyze regional differences in Second Life by measuring characteristics of the network as a whole, determined from the statistics mined from public conversations in the virtual world, rather than focusing on egocentric actors and their attributes.