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{ "item_title" : "Predicting Real World Behaviors from Virtual World Data", "item_author" : [" Muhammad Aurangzeb Ahmad", "Cuihua Shen", "Jaideep Srivastava "], "item_description" : "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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/3/31/934/849/3319348493_b.jpg", "price_data" : { "retail_price" : "54.99", "online_price" : "54.99", "our_price" : "54.99", "club_price" : "54.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Predicting Real World Behaviors from Virtual World Data|Muhammad Aurangzeb Ahmad

Predicting Real World Behaviors from Virtual World Data

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Overview

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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Details

  • ISBN-13: 9783319348490
  • ISBN-10: 3319348493
  • Publisher: Springer
  • Publish Date: September 2016
  • Dimensions: 9.21 x 6.14 x 0.29 inches
  • Shipping Weight: 0.44 pounds
  • Page Count: 118

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