Predicting Real World Behaviors from Virtual World Data (Springer Proceedings in Complexity)

Ahmad, Muhammad Aurangzeb (EDT)/ Shen, Cuihua (EDT)/ Srivastava, Jaideep (EDT)

Springer International Publishing AG（2016/09発売）

• 製本 Paperback:紙装版/ペーパーバック版／ページ数 118 p.
• 言語 ENG
• 商品コード 9783319348490
• DDC分類 004

Full 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.

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.