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Müller, S; Kapadia, M; Frey, S; Klinger, S; Mann, RP; Solenthaler, B; Sumner, RW; Gross, M (2015)
Publisher: Society for the Advancement of the Science of Digital Games
Languages: English
Types: Other
Interactive Virtual Worlds offer new individual and social experiences in a huge variety of artificial realities. They also have enormous potential for the study of how people interact, and how societies function and evolve. Systematic collection and analysis of in-play behavioral data will be invaluable for enhancing player experiences, facilitating effective administration, and unlocking the scientific potential of online societies. This paper details the development of a framework to collect player data in Minecraft. We present a complete solution which can be deployed on Minecraft servers to send collected data to a centralized server for visualization and analysis by researchers, players, and server administrators. Using the framework, we collected and analyzed over 14 person-days of active gameplay. We built a classification tool to identify high-level player behaviors from observations of their moment-by-moment game actions. Heat map visualizations highlighting spatial behavior can be used by players and server administrators to evaluate game experiences. Our data collection and analysis framework offers the opportunity to understand how individual behavior, environmental factors, and social systems interact through large-scale observational studies of virtual worlds.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] S. Aral, L. Muchnik, and A. Sundararajan. Distinguishing in uence-based contagion from homophily-driven di usion in dynamic networks. Proceedings of the National Academy of Sciences of the United States of America, 106(51):21544{21549, Dec. 2009.
    • [2] W. S. Bainbridge. The scienti c research potential of virtual worlds. science, 317(5837):472{476, 2007.
    • [3] R. Bartle. Hearts, clubs, diamonds, spades: Players who suit muds. MUD Research, 1(1):19, 1996.
    • [4] R. M. Bond, C. J. Fariss, J. J. Jones, A. D. I. Kramer, C. Marlow, J. E. Settle, and J. H. Fowler. A 61-million-person experiment in social in uence and political mobilization. Nature, 489(7415):295{298, Sept. 2012.
    • [5] E. Castronova. Virtual Worlds: A First-Hand Account of Market and Society on the Cyberian Frontier. papers.ssrn.com.
    • [6] E. Castronova, T. L. Ross, and I. Knowles. Designer, analyst, tinker: How game analytics will contribute to science. In Game Analytics, pages 665{687. Springer, 2013.
    • [7] E. Castronova, D. Williams, C. Shen, R. Ratan, L. Xiong, Y. Huang, and B. Keegan. As real as real? Macroeconomic behavior in a large-scale virtual world. New Media & Society, 11(5):685{707, July 2009.
    • [8] I. Constantiou, M. F. Legarth, and K. B. Olsen. What are users' intentions towards real money trading in massively multiplayer online games? . Electron Markets, 22:105{115, June 2012.
    • [9] L. Coviello, Y. Sohn, A. D. I. Kramer, C. Marlow, M. Franceschetti, N. A. Christakis, and J. H. Fowler. Detecting Emotional Contagion in Massive Social Networks. PloS one, 9(3):e90315, Mar. 2014.
    • [10] M. Csikszentmihalyi and R. Larson. Validity and reliability of the experience-sampling method. The Journal of nervous and mental disease, 175(9):526{536, 1987.
    • [11] N. Ducheneaut and N. Yee. Data collection in massively multiplayer online games: Methods, analytic obstacles, and case studies. In Game Analytics, pages 641{664. Springer, 2013.
    • [12] J. H. Fowler, N. A. Christakis, Steptoe, and D. Roux. Dynamic spread of happiness in a large social network: Longitudinal analysis of the Framingham Heart Study social network. BMJ: British Medical Journal, pages 23{27, 2009.
    • [13] R. Garnett, T. Gartner, T. Ellersiek, E. Gudmondsson, and P. Oskarsson. Predicting unexpected in uxes of players in eve online. In Computational Intelligence and Games (CIG), 2014 IEEE Conference on, pages 1{8. IEEE, 2014.
    • [14] R. Houlette. Player modeling for adaptive games. AI Game Programming Wisdom II, pages 557{566, 2004.
    • [15] M. Kapadia, J. Falk, F. Zund, M. Marti, R. W. Sumner, and M. Gross. Computer-assisted authoring of interactive narratives. In Proceedings of the 19th Symposium on Interactive 3D Graphics and Games, i3D '15, pages 85{92, New York, NY, USA, 2015. ACM.
    • [16] M. Kapadia, S. Singh, G. Reinman, and P. Faloutsos. A behavior-authoring framework for multiactor simulations. Computer Graphics and Applications, IEEE, 31(6):45 {55, nov.-dec. 2011.
    • [17] M. Kapadia, F. Zund, J. Falk, M. Marti, R. W. Sumner, and M. Gross. Evaluating the authoring complexity of interactive narratives for augmented reality applications. In Proceedings of the Foundations of Digital Games Conference, FDG '15, 2015.
    • [18] A. D. I. Kramer, J. E. Guillory, and J. T. Hancock. Experimental evidence of massive-scale emotional contagion through social networks. Proceedings of the National Academy of Sciences of the United States of America, 111(24):8788{8790, June 2014.
    • [19] L. Muchnik, S. Aral, and S. J. Taylor. Social In uence Bias: A Randomized Experiment. Science, 341(6146):647{651, Aug. 2013.
    • [20] C. K. Olson. Children's motivations for video game play in the context of normal development. Review of General Psychology, 14(2):180{187, 2010.
    • [21] H. Prendinger, J. Mori, and M. Ishizuka. Recognizing, modeling, and responding to users' a ective states. In L. Ardissono, P. Brna, and A. Mitrovic, editors, User Modeling 2005, volume 3538 of Lecture Notes in Computer Science, pages 60{69. Springer Berlin Heidelberg, 2005.
    • [22] A. Shoulson, M. L. Gilbert, M. Kapadia, and N. I. Badler. An event-centric planning approach for dynamic real-time narrative. In Proceedings of Motion on Games, MIG '13, pages 99:121{99:130, New York, NY, USA, 2013. ACM.
    • [23] D. Thue, V. Bulitko, M. Spetch, and E. Wasylishen. Interactive storytelling: a player modelling approach. In Proceedings of the 3rd AAAI International Conference on Arti cial Intelligence and Interactive Digital Entertainment, pages 43{48, 2007.
    • [24] S. Villani. Impact of media on children and adolescents: A 10-year review of the research. Journal of the American Academy of Child & Adolescent Psychiatry, 40(4):392{401, 2001.
    • [25] M. E. Wollslager. Children's awareness of online advertising on Neopets: The e ect of media literacy training on recall. SIMILE: Studies In Media & Information Literacy Education, 9(2):31{53, 2009.
    • [26] G. Yannakakis and M. Maragoudakis. Player modeling impact on player's entertainment in computer games. In L. Ardissono, P. Brna, and A. Mitrovic, editors, User Modeling 2005, volume 3538 of Lecture Notes in Computer Science, pages 74{78. Springer Berlin Heidelberg, 2005.
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    The results below are discovered through our pilot algorithms. Let us know how we are doing!

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