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.
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Acknowledgments
This research was supported by the National Science Foundation (NSF IIS-0729421), the Army Research Institute (ARI W91WAW-08-C-0106), Air Force Research Lab (AFRL Contract No. FA8650-10-C-7010), the Army Research Lab (ARL) Network Science—Collaborative Technology Alliance (NS-CTA) via BBN TECH/W911NF-09-2-0053, and the National Science Foundation via the XSEDE project’s Extended Collaborative Support Service under Grant # NSF-OCI 1053575. The data used for this research was provided by the SONY Online Entertainment. The findings represent solely the opinions of the authors and not of the sponsors.
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Ahmed, I., Mahapatra, A., Poole, M., Srivastava, J., Brown, C. (2014). Identifying a Typology of Players Based on Longitudinal Game Data. In: Ahmad, M., Shen, C., Srivastava, J., Contractor, N. (eds) Predicting Real World Behaviors from Virtual World Data. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-07142-8_7
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DOI: https://doi.org/10.1007/978-3-319-07142-8_7
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