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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.

Inhaltsverzeichnis

Frontmatter

On the Problem of Predicting Real World Characteristics from Virtual Worlds

Abstract
Availability of massive amounts of data about the social and behavioral characteristics of a large subset of the population opens up new possibilities that allow researchers to not only observe people’s behaviors in a natural, rather than artificial, environment but also conduct predictive modeling of those behaviors and characteristics. Thus an emerging area of study is the prediction of real world characteristics and behaviors of people in the offline or “real” world based on their behaviors in the online virtual worlds. We explore the challenges and opportunities in the emerging field of prediction of real world characteristics based on people’s virtual world characteristics, i.e., what are the major paradigms in this field, what are the limitations in current predictive models, limitations in terms of generalizability, etc. Lastly, we also address the future challenges and avenues of research in this area.
Muhammad Aurangzeb Ahmad, Cuihua Shen, Jaideep Srivastava, Noshir Contractor

The Use of Social Science Methods to Predict Player Characteristics from Avatar Observations

Abstract
The purpose of this study was to investigate the extent to which real world characteristics of massively multiplayer online role-playing game (MMORPG) players can be predicted based on the characteristics and behavior of their avatars. Ground truth on participants’ real world characteristics was obtained through the administration of validated measures of personality and authoritarian ideology, as well as a demographics form. A team of trained assessors used quantitative assessment instruments to evaluate avatar characteristics, behavior, and personality from a recorded session of the participant’s typical gameplay. The statistical technique of discriminant analysis was then applied to create predictive models for players’ real world characteristics such as gender, approximate age, and education level, using the variables generated through observational assessment of the avatar.
Carl Symborski, Gary M. Jackson, Meg Barton, Geoffrey Cranmer, Byron Raines, Mary Magee Quinn

Analyzing Effects of Public Communication onto Player Behavior in Massively Multiplayer Online Games

Abstract
In this preliminary work, we study how public forum communication reflects and shapes virtual world behavior. We find that in-game groups have differential public posting habits; that player behavior is reflected in public communication (in particular, players who attack more are mentioned more in the public forums), and finally that public and personal communications are linked, those who speak together publicly also speak together privately.
Kiran Lakkaraju, Jeremy Bernstein, Jon Whetzel

Identifying User Demographic Traits Through Virtual-World Language Use

Abstract
The paper presents approaches for identifying real-world demographic attributes based on language use in the virtual world. We apply features developed from the classic literature on sociolinguistics and sound symbolism to data collected from virtual-world chat and avatar naming to determine participants’ age and gender. We also examine participants’ use of avatar names across virtual worlds and how these names are employed to project a consistent identity across environments, which we call “traveling characteristics.”
Aaron Lawson, John Murray

Predicting MMO Player Gender from In-Game Attributes Using Machine Learning Models

Abstract
What in-game attributes predict players’ offline gender? Our research addresses this question using behavioral logs of over 4,000 EverQuest II players. The analysis compares four variable sets with multiple combinations of character types (avatar characteristics or gameplay behaviors; primary or nonprimary character), three server types within the game (roleplaying, player-vs-player, and player-vs-environment), and three types of predictive machine learning models (JRip, J48, and Random Tree). Overall, the most highly predictive, interpretable model has an f-measure of 0.94 and suggests the primary character gender and number of male and female characters a player has provide the most prediction value, with players choosing characters to match their own gender. The results also suggest that female players craft, scribe recipes, and harvest items more than male players. While the strength of these findings varies by server type, they are generally consistent with previous research and suggest that players tend to play in ways that are consistent with their offline identities.
Tracy Kennedy, Rabindra (Robby) Ratan, Komal Kapoor, Nishith Pathak, Dmitri Williams, Jaideep Srivastava

Predicting Links in Human Contact Networks Using Online Social Proximity

Abstract
Experimentally measured contact traces, such as those obtained through short range wireless sensors, have allowed researchers to study how mobile users contact each other in different environments. These traces often include other types of useful information such as users’ social profiles and their online friend lists. This explicit social information is important since it can be exploited for augmenting the knowledge of user behavior and hence improve the quality of human mobility analysis. In this paper, we use online social ties for predicting users’ contacts. Specifically, we study the prediction of links in human contact networks as a graph inference problem, where the existence of an edge is predicted using different proximity measures that quantify the closeness or similarity between nodes. First, we predict the edges of the contact graph when we have only information about users’ online social network. Next, we analyze the effectiveness of using both the online social network and a part of the contact network for contact prediction. In both settings, our study on three different human contact traces shows that resource allocation measure plays a significant role in contact prediction. Furthermore, the results demonstrate the importance of online social proximity in identifying stronger ties.
Annalisa Socievole, Floriano De Rango, Salvatore Marano

Identifying a Typology of Players Based on Longitudinal Game Data

Abstract
This study describes an approach to identify a typology of players based on longitudinal game data. The study explored anonymous user log data of 1854 players of EverQuest II (EQII)—a massively multiplayer online game (MMOG). The study tracked ten specific in-game player behavior including types of activities, activity related rewards, and casualties for 27 weeks. The objective of the study was to understand player characteristics and behavior from longitudinal data. Primary analysis revealed meaningful typologies, differences among players based on identified typologies, and differences between individual and group related gaming situations.
Iftekhar Ahmed, Amogh Mahapatra, Marshall Scott Poole, Jaideep Srivastava, Channing Brown

Backmatter

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