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.
@book{ahmad2014predicting,title={Predicting real world behaviors from virtual world data},author={Ahmad, Muhammad Aurangzeb and Shen, Cuihua and Srivastava, Jaideep and Contractor, Noshir},year={2014},publisher={Springer},}
WSDM
Behavioral data mining and network analysis in massive online games
Muhammad Aurangzeb
Ahmad, and Jaideep
Srivastava
In Proceedings of the 7th ACM international conference on web search and data mining, 2014
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).
@inproceedings{ahmad2014behavioral,title={Behavioral data mining and network analysis in massive online games},author={Ahmad, Muhammad Aurangzeb and Srivastava, Jaideep},booktitle={Proceedings of the 7th ACM international conference on web search and data mining},pages={673--674},year={2014},}
Encyclopedia
Dark Sides of Social Networking
Brian C
Keegan, and Muhammad Aurangzeb
Ahmad
In Encyclopedia of Social Network Analysis and Mining, 2014
@incollection{keegan2014dark,title={Dark Sides of Social Networking},author={Keegan, Brian C and Ahmad, Muhammad Aurangzeb},booktitle={Encyclopedia of Social Network Analysis and Mining},pages={319--332},year={2014},}
Book Chapter
On the problem of predicting real world characteristics from virtual worlds
Muhammad Aurangzeb
Ahmad, Cuihua
Shen, Jaideep
Srivastava, and
1 more author
In Predicting real world behaviors from virtual world data, 2014
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.
@inproceedings{ahmad2014problem,title={On the problem of predicting real world characteristics from virtual worlds},author={Ahmad, Muhammad Aurangzeb and Shen, Cuihua and Srivastava, Jaideep and Contractor, Noshir},booktitle={Predicting real world behaviors from virtual world data},pages={1--18},year={2014},organization={Springer},}
2013
ASONAM
Guilt by association? Network based propagation approaches for gold farmer detection
Muhammad Aurangzeb
Ahmad, Brian
Keegan, Atanu
Roy, and
3 more authors
In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 2013
The term ’Gold Farmer’ refers to a class of players in massive online games (MOGs) involved in a set of interrelated activities which are considered to be deviant activities. Consequently these gold farmers are actively banned by game administrators. The task of gold farmer detection is to identify gold farmers in a population of players but just like other clandestine actors they not labeled as such. In this paper the problem of extending the label of gold farmers to players which are not labeled as such is considered. Two main classes of techniques are described and evaluated: Network-based approaches and similarity based approaches. It is also explored how dividing the problem further by relabeling the data based on behavioral patterns can further improve the results
@inproceedings{ahmad2013guilt,title={Guilt by association? Network based propagation approaches for gold farmer detection},author={Ahmad, Muhammad Aurangzeb and Keegan, Brian and Roy, Atanu and Williams, Dmitri and Srivastava, Jaideep and Contractor, Noshir},booktitle={Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},pages={121--126},year={2013},url={https://dl.acm.org/doi/abs/10.1145/2492517.2492649},}
Patent
Automatic detection of deviant players in massively multiplayer online role playing games (mmogs)
Dmitri
Williams, Muhammad Aurangzeb
Ahmad, Jaideep
Srivastava, and
2 more authors
Gold farming refers to the illicit practice of gathering and selling virtual goods in online games for real money. Although around one million gold farmers engage in gold farming related activities, to date a systematic study of identifying gold farmers has not been done. Here data is used from the Massively Multiplayer Online Role Playing Game (MMOG) EverQuest II to identify gold farmers. This is posed as a binary classification problem and a set of features is identified for classification purposes. Given the cost associated with investigating gold farmers, criteria are also given for evaluating gold farming detection techniques, and suggestions provided for future testing and evaluation techniques.
@misc{williams2013automatic,title={Automatic detection of deviant players in massively multiplayer online role playing games (mmogs)},author={Williams, Dmitri and Ahmad, Muhammad Aurangzeb and Srivastava, Jaideep and Keegan, Brian and Contractor, Noshir},year={2013},publisher={Google Patents},note={US Patent App. 13/401,541},}
2012
Social Computing
The ones that got away: False negative estimation based approaches for gold farmer detection
Atanu
Roy, Muhammad Aurangzeb
Ahmad, Chandrima
Sarkar, and
2 more authors
In 2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing, 2012
The problem of gold farmer detection is the problem of detecting players with illicit behaviors in massively multiplayer online games (MMOs) and has been studied extensively. Detecting gold farmers or other deviant actors in social systems is traditionally understood as a binary classification problem, but the issue of false negatives is significant for administrators as residual actors can serve as the backbone for subsequent clandestine organizing. In this paper we address this gap in the literature by addressing the problem of false negative estimation for gold farmers in MMOs by employing the capture-recapture technique for false negative estimation and combine it with graph clustering techniques to determine "hidden" gold farmers in social networks of farmers and normal players. This paper redefines the problem of gold farming as a false negative estimation problem and estimates the gold farmers in co-extensive MMO networks, previously undetected by the game administrators. It also identifies these undetected gold farmers using graph partitioning techniques and applies network data to address rare class classification problem. The experiments in this research found 53% gold farmers who were previously undetected by the game administrators.
@inproceedings{roy2012ones,title={The ones that got away: False negative estimation based approaches for gold farmer detection},author={Roy, Atanu and Ahmad, Muhammad Aurangzeb and Sarkar, Chandrima and Keegan, Brian and Srivastava, Jaideep},booktitle={2012 International Conference on Privacy, Security, Risk and Trust and 2012 International Confernece on Social Computing},pages={328--337},year={2012},organization={IEEE},url={https://ieeexplore.ieee.org/abstract/document/6406262},}
Trust is a ubiquitous phenomenon in human societies. Computational trust refers to the mediation of trust via a computational infrastructure. It has been studied in a variety of contexts e.g., peer-to-peer systems, multi-agent systems, recommendation systems etc. While this is an active area of research, the types of questions that have been explored in this eld has been limited mainly because of limitations in the types of datasets which are available to researchers. In this thesis questions related to trust in complex social environments represented by Massively Multiplayer Online Games (MMOGs) are explored. The main emphasis is that trust is a multi-level phenomenon both in terms of how it operates at multiple levels of network granularities and how trust relates to other social phenomenon like homophily, expertise, mentoring, clandestine behaviors etc. Social contexts and social environments aect not just the qualitative aspects of trust but this phenomenon is also manifested with respect to the network and structural signatures of trust network Additionally trust is also explored in the context of predictive tasks: Previously described prediction tasks like link prediction are studied in the context of trust within the context of the link prediction family of problems: Link formation, link breakage, change in links etc. Additionally we dene and explore new trust-related prediction problems i.e., trust propensity prediction, trust prediction across networks which can be generalized to the inter-network link prediction problem and success prediction based on using network measures of a person’s social capital as a proxy.
@book{ahmad2012computational,title={Computational trust in multiplayer online games},author={Ahmad, Muhammad Aurangzeb},year={2012},publisher={University of Minnesota},url={https://ieeexplore.ieee.org/abstract/document/6406262},}
2011
Social Computing
Illicit bits: Detecting and analyzing contraband networks in Massively Multiplayer Online Games
Muhammad Aurangzeb
Ahmad, Brian
Keegan, Sophia
Sullivan, and
3 more authors
In 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing, 2011
Although trade in illicit items and services is prevalent in many economic systems, collecting reliable data and making empirical claims about this activity is difficult. Using anonymized behavioral logs from a massively multiplayer online game, we analyze the items exchanged by players later banned for gold farming. We simultaneously analyze clandestine social networks of deviant players in MMOGs as well the network of contraband items that are sold by these players. The insights from the network analysis are used to build predictive models for identifying deviant players in the clandestine networks. We show that the results obtained from our proposed approach are far superior to the state of the art for such clandestine networks. Additionally we observed that the contraband networks contain certain type of objects which are not found in their "normal" counterparts.
@inproceedings{ahmad2011illicit,title={Illicit bits: Detecting and analyzing contraband networks in Massively Multiplayer Online Games},author={Ahmad, Muhammad Aurangzeb and Keegan, Brian and Sullivan, Sophia and Williams, Dmitri and Srivastava, Jaideep and Contractor, Noshir},booktitle={2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing},pages={127--134},year={2011},organization={IEEE},}
AAAI
Towards analyzing adversarial behavior in clandestine networks
Muhammad Aurangzeb
Ahmad, Brian
Keegan, Sophia
Sullivan, and
3 more authors
In Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence, 2011
Adversarial behavioral has been observed in many different contexts. In this paper we address the problem of adversarial behavior in the context of clandestine networks. We use data from a massively multiplayer online role playing game to illustrate the behavioral and structural signatures of deviant players change over time as a response to" policing" activities of the game administrators. Preliminary results show that the behavior of the deviant players and their affiliates show co-evolutionary behavior and the timespan within the game can be divided into different epochs based on their behaviors. Feature sets derived from these results can be used for better predictive machine learning models for detecting deviants in clandestine networks.
@inproceedings{ahmad2011towards,title={Towards analyzing adversarial behavior in clandestine networks},author={Ahmad, Muhammad Aurangzeb and Keegan, Brian and Sullivan, Sophia and Williams, Dmitri and Srivastava, Jaideep and Contractor, Noshir},booktitle={Workshops at the Twenty-Fifth AAAI Conference on Artificial Intelligence},year={2011},}
Web Science
Sic transit gloria mundi virtuali? Promise and peril in the computational social science of clandestine organizing
Brian
Keegan, Muhammad Aurangzeb
Ahmed, Dmitri
Williams, and
2 more authors
In Proceedings of the 3rd International Web Science Conference, 2011
Massively multiplayer online games (MMOGs) maintain archival databases of all player actions and attributes including activity by accounts engaged in illicit behavior. If individuals in online worlds operate under similar social and psychological motivations and constraints as the offline world, online behavioral data could inform theories about offline behavior. We examine high risk trading relationships in a MMOG to illuminate the structures online clandestine organizations employ to balance security with efficiency and compare this to an offline drug trafficking network. This data offers the possibility of performing social research on a scale that would be unethical or impracticable to do in the offline world. However, analyzing and generalizing from clandestine behavior in online settings raises complex epistemological and methodological questions about the validity of such mappings and what methods and metrics are appropriate in these contexts. We conclude by discussing how computational social science can be applied to online and offline criminological concerns and highlight the “dual use” implications of these technologies.
@inproceedings{keegan2011sic,title={Sic transit gloria mundi virtuali? Promise and peril in the computational social science of clandestine organizing},author={Keegan, Brian and Ahmed, Muhammad Aurangzeb and Williams, Dmitri and Srivastava, Jaideep and Contractor, Noshir},booktitle={Proceedings of the 3rd International Web Science Conference},pages={1--8},year={2011},}
First Monday
Focused on the prize: Characteristics of experts in massive multiplayer online games
Jing
Wang, David A
Huffaker, Jeffrey W
Treem, and
5 more authors
This study is the first large–scale multi–method attempt to empirically examine the characteristics leading to development of expertise in EverQuest II, a popular massively multi–player online role–playing game (MMOs). Benefiting from the unprecedented opportunity of obtaining game log data matched with survey data, the project investigated the relationship between player motivations and in–game behavior, personality characteristics, and demographic attributes with game performance and achievement, which we refer to as game “expertise.” Players who were high on achievement motivation or social motivation had higher game expertise, while those high on immersion motivation had lower expertise. Game experts were also characterized by focusing their game time on completing tasks. Younger players showed a slim advantage over older players. Male and female players exhibited similar expertise levels in this MMO.
@article{wang2011focused,title={Focused on the prize: Characteristics of experts in massive multiplayer online games},author={Wang, Jing and Huffaker, David A and Treem, Jeffrey W and Fullerton, Lindsay and Ahmad, Muhammad A and Williams, Dmitri and Poole, Marshall Scott and Contractor, Noshir},journal={First Monday},year={2011},}
