Predicting Real World Behaviors from Virtual World Data
Published on 7. August 2014
Book
Hardback
XIV, 118 pages
978-3-319-07141-1 (ISBN)
Description
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. The 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.
More details
Series
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Research
Illustrations
13 s/w Abbildungen, 27 farbige Abbildungen
XIV, 118 p. 40 illus., 27 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 13 mm
Weight
371 gr
ISBN-13
978-3-319-07141-1 (9783319071411)
DOI
10.1007/978-3-319-07142-8
Schweitzer Classification
Other editions
Additional editions
Muhammad Aurangzeb Ahmad | Cuihua Shen | Jaideep Srivastava
Predicting Real World Behaviors from Virtual World Data
Book
09/2016
Springer
€53.49
Shipment within 10-15 days
Muhammad Aurangzeb Ahmad | Cuihua Shen | Jaideep Srivastava
Predicting Real World Behaviors from Virtual World Data
E-Book
07/2014
1st Edition
Springer
€53.49
Available for download
Content
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.