Virtual worlds refer to shared persistent massive online spaces where hundreds of thousands and even millions of people can interact with one another. Examples of such spaces include massive multiplayer online games like World of Warcraft, Eve Online etc and structured environments like SecondLife. There is a growing body of literature that focuses on the similarities and differences between how people behave in the offline world vs. how they behave in these virtual environments. Data mining has aided in discovering interesting insights with respect to how people behave in these virtual environments. Given that there is a vigorous debate at the heart of this domain with respect to the methodological limitations and the limitations of inferences across the boundaries of the real and the virtual that there is a need for a workshop that provides a common platform for discussion of challenging problems and potential solutions in this emergence field. The Workshop on Predicting Real World Behaviors from Virtual Worlds Data (VRVW) workshop will provide a critical and essential forum for integrating various research challenges in this domain and promote collaboration among researchers from academia and industry to enhance the state-of-art and help define a clear path for future research in this emerging area. This workshop is interested in research that seeks to bridge the divide between connecting online and offline behaviors. Topics of interest include prediction, mining and analysis of offline characteristics and behaviors from online data and vice versa. This workshop will facilitate collaboration among different disciplines including computer science, game studies and the social sciences.