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Predicting Real World Behaviors from Virtual World Data.

By: Ahmad, Muhammad Aurangzeb.
Contributor(s): Shen, Cuihua | Srivastava, Jaideep | Contractor, Noshir.
Material type: TextTextSeries: eBooks on Demand.Springer Proceedings in Complexity: Publisher: Dordrecht : Springer, 2014Description: 1 online resource (127 p.).ISBN: 9783319071428.Subject(s): Android (Electronic resource) | Augmented reality | Human-computer interactionGenre/Form: Electronic books.Additional physical formats: Print version:: Predicting Real World Behaviors from Virtual World DataDDC classification: 006.8 LOC classification: QA76.76 .A65Online resources: Click here to view this ebook.
Contents:
Preface; Contents; Contributors; On the Problem of Predicting Real World Characteristics from Virtual Worlds; 1 Introduction; 2 The Mapping Principle; 3 Theory-Driven versus Data-Driven Paradigms; 4 Limitations and Methodological Issues; 4.1 Data Quality; 4.2 Generalization; 4.3 Surveys, Perception, and Truth; 4.4 Mismapping from Virtual Worlds to the Real World; 5 Case Studies: Virtual to Real World Mappings; 5.1 Case Study: Virtual Economy; 5.2 Case Study: Epidemiology; 5.3 Case Study: Deviant Clandestine Behaviors; 5.4 Case Study: Mentoring
6 Predictive Modeling from Virtual World(s) to Real World(s)7 Conclusions; References; The Use of Social Science Methods to Predict Player Characteristics from Avatar Observations; 1 Introduction; 2 Method; 2.1 Participants; 2.2 Instruments; 2.2.1 Data Collection Instruments---RW Characteristics; 2.2.2 Data Processing Instruments---Avatar Characteristics and Behavior; 2.3 Design and Procedure; 2.3.1 Laboratory/Home Session; 2.3.2 Quantitative Data Processing; 2.3.3 Statistical Technique: DA; 3 Results and Discussion; 3.1 The Discriminant Function; 3.2 Accuracy Metrics; 3.3 Overall Results
3.4 Gender3.4.1 Definition of the DV; 3.4.2 Accuracy of Gender Model; 3.4.3 Discriminant Function for Gender; 3.4.4 Discussion of IVs Relevant to the Prediction of Gender; Avatar Gender (ACF); Majority Role of Support (ACF); Covered Hair (ACF); 3.5 Age; 3.5.1 Definition of the DV; 3.5.2 Accuracy of Age Model; 3.5.3 Discriminant Function for Age; 3.5.4 Discussion of IVs Relevant to the Prediction of Age; Avatar does not move for the full 60 s (VW-BAF); Unconventional Hair (ACF); ``She often feels tense and jittery.''* (NEO-R); 3.6 Education Level; 3.6.1 Definition of the DV
3.6.2 Accuracy of Education Level Model3.6.3 Discriminant Function for Education Level Model; 3.6.4 Discussion of IVs Relevant to the Prediction of Education Level; Role of Ranged Control (ACF); Number of curse words or insults (VW-BAF); 3.7 Extraversion Level; 3.7.1 Definition of the DV; 3.7.2 Accuracy of Extraversion Level Model; 3.7.3 Discriminant Function for Extraversion Level; 3.7.4 Discussion of IVs Relevant to the Prediction of Extraversion Level; ``He laughs easily.''* (NEO-R); Role of Ranged DPS (ACF); ``Some people think he is selfish and egotistical.''* (NEO-R)
3.8 Submissive Ideology3.8.1 Definition of the DV; 3.8.2 Accuracy of Submissive Ideology Model; 3.8.3 Discriminant Function for Submissive Ideology Model; 3.8.4 Discussion of IVs Relevant to the Prediction of Submissive Ideology; ``She tends to be cynical and skeptical of others' intentions.''* (NEO-R); Player engages in PvP (ACF); 4 Conclusions; References; Analyzing Effects of Public Communication onto Player Behavior in Massively Multiplayer Online Games; 1 Introduction; 2 Related Work; 3 Description of Game X; 3.1 Groups in Game X; 3.2 Nations; 3.3 Agency; 3.4 Race; 3.5 Guild
3.5.1 Communication in Game X
Summary: 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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Item type Current location Call number URL Status Date due Barcode
Electronic Book UT Tyler Online
Online
QA76.76 .A65 (Browse shelf) http://uttyler.eblib.com/patron/FullRecord.aspx?p=1783017 Available EBL1783017

Preface; Contents; Contributors; On the Problem of Predicting Real World Characteristics from Virtual Worlds; 1 Introduction; 2 The Mapping Principle; 3 Theory-Driven versus Data-Driven Paradigms; 4 Limitations and Methodological Issues; 4.1 Data Quality; 4.2 Generalization; 4.3 Surveys, Perception, and Truth; 4.4 Mismapping from Virtual Worlds to the Real World; 5 Case Studies: Virtual to Real World Mappings; 5.1 Case Study: Virtual Economy; 5.2 Case Study: Epidemiology; 5.3 Case Study: Deviant Clandestine Behaviors; 5.4 Case Study: Mentoring

6 Predictive Modeling from Virtual World(s) to Real World(s)7 Conclusions; References; The Use of Social Science Methods to Predict Player Characteristics from Avatar Observations; 1 Introduction; 2 Method; 2.1 Participants; 2.2 Instruments; 2.2.1 Data Collection Instruments---RW Characteristics; 2.2.2 Data Processing Instruments---Avatar Characteristics and Behavior; 2.3 Design and Procedure; 2.3.1 Laboratory/Home Session; 2.3.2 Quantitative Data Processing; 2.3.3 Statistical Technique: DA; 3 Results and Discussion; 3.1 The Discriminant Function; 3.2 Accuracy Metrics; 3.3 Overall Results

3.4 Gender3.4.1 Definition of the DV; 3.4.2 Accuracy of Gender Model; 3.4.3 Discriminant Function for Gender; 3.4.4 Discussion of IVs Relevant to the Prediction of Gender; Avatar Gender (ACF); Majority Role of Support (ACF); Covered Hair (ACF); 3.5 Age; 3.5.1 Definition of the DV; 3.5.2 Accuracy of Age Model; 3.5.3 Discriminant Function for Age; 3.5.4 Discussion of IVs Relevant to the Prediction of Age; Avatar does not move for the full 60 s (VW-BAF); Unconventional Hair (ACF); ``She often feels tense and jittery.''* (NEO-R); 3.6 Education Level; 3.6.1 Definition of the DV

3.6.2 Accuracy of Education Level Model3.6.3 Discriminant Function for Education Level Model; 3.6.4 Discussion of IVs Relevant to the Prediction of Education Level; Role of Ranged Control (ACF); Number of curse words or insults (VW-BAF); 3.7 Extraversion Level; 3.7.1 Definition of the DV; 3.7.2 Accuracy of Extraversion Level Model; 3.7.3 Discriminant Function for Extraversion Level; 3.7.4 Discussion of IVs Relevant to the Prediction of Extraversion Level; ``He laughs easily.''* (NEO-R); Role of Ranged DPS (ACF); ``Some people think he is selfish and egotistical.''* (NEO-R)

3.8 Submissive Ideology3.8.1 Definition of the DV; 3.8.2 Accuracy of Submissive Ideology Model; 3.8.3 Discriminant Function for Submissive Ideology Model; 3.8.4 Discussion of IVs Relevant to the Prediction of Submissive Ideology; ``She tends to be cynical and skeptical of others' intentions.''* (NEO-R); Player engages in PvP (ACF); 4 Conclusions; References; Analyzing Effects of Public Communication onto Player Behavior in Massively Multiplayer Online Games; 1 Introduction; 2 Related Work; 3 Description of Game X; 3.1 Groups in Game X; 3.2 Nations; 3.3 Agency; 3.4 Race; 3.5 Guild

3.5.1 Communication in Game X

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.

Description based upon print version of record.

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