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Predicting Real World Behaviors from Virtual World Data de Muhammad Aurangzeb Ahmad

Predicting Real World Behaviors from Virtual World Data: Springer Proceedings in Complexity

Editat de Muhammad Aurangzeb Ahmad, Cuihua Shen, Jaideep Srivastava, Noshir Contractor
en Limba Engleză Hardback – 7 aug 2014
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.
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Specificații

ISBN-13: 9783319071411
ISBN-10: 3319071416
Pagini: 130
Ilustrații: XIV, 118 p. 40 illus., 27 illus. in color.
Dimensiuni: 155 x 235 x 12 mm
Greutate: 0.37 kg
Ediția:2014
Editura: Springer International Publishing
Colecția Springer
Seria Springer Proceedings in Complexity

Locul publicării:Cham, Switzerland

Public țintă

Research

Cuprins

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.

Textul de pe ultima copertă

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.

Caracteristici

Gathers insights from different disciplines like data mining, behavioral modeling, ethnography to connect the online with the offline world Features data-driven and theory-driven techniques for predicting people’s behavior in the real world Provides a framework for doing predictive modeling from virtual worlds to the real world and the efficacy of such predictions Includes supplementary material: sn.pub/extras