Predicting Real World Behaviors from Virtual World Data

• Corporate Author: SpringerLink (Online service)
• Other Authors: Ahmad, Muhammad Aurangzeb (Editor), Shen, Cuihua (Editor), Srivastava, Jaideep (Editor), Contractor, Noshir (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer International Publishing : Imprint: Springer, 2014.
• Series: Springer Proceedings in Complexity,
• Subjects: Computer science.; Application software.; Mathematics.; Social sciences.; Sociophysics.; Econophysics.; Computer Science.; Computer Appl. in Social and Behavioral Sciences.; Socio- and Econophysics, Population and Evolutionary Models.; Methodology of the Social Sciences.; Mathematics in the Humanities and Social Sciences.
• Physical Description: XIV, 118 p. 40 illus., 27 illus. in color. online resource.
• ISBN: 9783319071428
• ISSN: 2213-8684

This book addresses prediction, mining and analysis of offline characteristics and behaviors from online data and vice versa. Each chapter will focus on a different aspect of virtual worlds to real world prediction e.g., demographics, personality, location, etc. 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.