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Predicting Real World Behaviors from Virtual World Data
Title
Predicting Real World Behaviors from Virtual World Data [electronic resource] / edited by Muhammad Aurangzeb Ahmad, Cuihua Shen, Jaideep Srivastava, Noshir Contractor.
ISBN
9783319071428
Publication
Cham : Springer International Publishing : Imprint: Springer, 2014.
Physical Description
1 online resource (XIV, 118 p.) 40 illus., 27 illus. in color.
Local Notes
Access is available to the Yale community.
Access and use
Access restricted by licensing agreement.
Summary
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.
Variant and related titles
Springer ENIN.
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Format
Books / Online
Language
English
Added to Catalog
September 20, 2018
Series
Springer Proceedings in Complexity,
Contents
Preface
On The Problem of Predicting Real World Characteristics from Virtual Worlds
The Use of Social Science Methods to Predict Player Characteristics from Avatar Observations
Analyzing Effects of Public Communication onto Player Behavior in Massively Multiplayer Online Games
Identifying User Demographic Traits through Virtual-World Language Use
Predicting MMO Player Gender from In-Game Attributes using Machine Learning Models
Predicting Links in Human Contact Networks using Online Social Proximity
Identifying a Typology of Players Based on Longitudinal Game Data.
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