Automatically Detecting Points of Interest and Social Networks from Tracking Positions of Avatars in a Virtual World

With hundreds of millions of users already today,virtual worlds will become an important factor in tomorrow´s media landscape. In a virtual world, users are represented by so-called avatars. These avatars move around the virtual world, communicate with each other,and interact with the virtual world. The movements of these avatars can be tracked precisely, and useful information can be inferred from analyzing these movements. In this paper, we analyze a large data set (>200 million records) of position data describing the movements of avatars in the virtual world Second Life.The dataset was derived from in-world sensors that had been deployed beforehand, but also so-called bots can be used to gather such information. From this data, we can track usage patterns of avatars (and therefore users) overtime. We can also identify regions of high interest where a large number of users gather frequently (which would be important for planning advertising in the virtual world), and visualize this statistical analysis using heat maps. By combining the position data with information about the language spoken by the avatars, we can label these regions according to the language predominantly spoken there. Analyzing incidents of co-location of avatars over a period of time, we can automatically infer friends, and eventually social networks. Using additional metadata such as language we can label clusters in this automatically generated social network.