NDLI: Behavioral data mining and network analysis in massive online games

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Behavioral data mining and network analysis in massive online games

Content Provider	ACM Digital Library
Author	Srivastava, Jaideep Ahmad, Muhammad Aurangzeb
Abstract	The last decade has been characterized by an explosion of social media in a variety of forms. Since the data is captured in digital form it has become possible for the first time study human behavior at a massive scale. Not only is it possible to address traditional questions in the social sciences regarding collective dynamics of human behaviors but it is also possible to study new types of human behaviors which have arisen as a result of usage of new mediums like twitter, YouTube, Facebook, one games etc. Each of these mediums has its respective limitations and affordances. Out of all these mediums the most complex and data rich medium is that of Massive Online Games (MOGs). MOGs refer to massive online persistent environments (World of Warcraft, EVE Online, EverQuest etc) shared by millions of people . In general these environments are characterized by a rich array of activities and social interactions with a wide array of behaviors e.g., cooperation, trade, quest, deceit, mentoring etc. Such environments allow one to study human behavior at a level of granularity where it was not possible to do so previously. Given the challenges associated with analyzing this type of data traditional techniques in data mining and social network analysis have to be extended with insights from the social sciences. The tutorial will cover predictive and generative models in the study of MOGs. Additionally we will cover some SNA techniques which are more appropriate for MOGs given the multi-dimensionality of the data (P*/ERGM Models, IR Based Network Analysis, Hypergrah based Techniques, Coextensive Social Networks etc). We also describe the various ways in which MOGs exhibit similarities to the real world e.g., economic behaviors, clandestine behaviors, mentoring etc).
Starting Page	673
Ending Page	674
Page Count	2
File Format	PDF
ISBN	9781450323512
DOI	10.1145/2556195.2556196
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-02-24
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Behavioral mining Game analytics Social network analysis
Content Type	Text
Resource Type	Article

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