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Predicting Social Ties in Massively Multiplayer Online Games

  • Jina Lee
  • Kiran Lakkaraju
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8393)

Abstract

Social media has allowed researchers to induce large social networks from easily accessible online data. However, relationships inferred from social media data may not always reflect the true underlying relationship. The main question of this work is: How does the public social network reflect the private social network? We begin to address this question by studying interactions between players in a Massively Multiplayer Online Game. We trained a number of classifiers to predict the social ties between players using data on public forum posts, private messages exchanged between players, and their relationship information. Results show that using public interaction knowledge significantly improves the prediction of social ties between two players and including a richer set of information on their relationship further improves this prediction.

Keywords

Support Vector Machine Social Medium Social Medium Data Large Social Network Game World 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Gilbert, E., Karahalios, K.: Predicting tie strength with social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI 2009, pp. 211–220. ACM, New York (2009)Google Scholar
  2. 2.
    Reis, H.T., Judd, C.M.: Handbook of research methods in social and personality psychology. Cambridge University Press (2000)Google Scholar
  3. 3.
    Yee, N., Ducheneaut, N., Nelson, L., LIkarish, P.: Introverted elves & conscientious gnomes: The expression of personality in world of warcraft. In: Proceedings of CHI 2011 (2011)Google Scholar
  4. 4.
    Castronova, E., Williams, D., Shen, C., Ratan, R., Xiong, L., Huang, Y., Keegan, B.: As real as real? macroeconomic behavior in a large-scale virtual world. New Media and Society 11(5), 685–707 (2009)CrossRefGoogle Scholar
  5. 5.
    Leskovec, J., Huttenlocher, D., Kleinberg, J.: Predicting positive and negative links in online social networks. In: Proceedings of the 19th International Conference on World Wide Web, WWW 2010, pp. 641–650. ACM, New York (2010)CrossRefGoogle Scholar
  6. 6.
    Chen, H.-H., Gou, L., Zhang, X.(L.), Giles, C.L.: Predicting recent links in FOAF networks. In: Yang, S.J., Greenberg, A.M., Endsley, M. (eds.) SBP 2012. LNCS, vol. 7227, pp. 156–163. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  7. 7.
    Liben-Nowell, D., Kleinberg, J.: The link-prediction problem for social networks. Journal of American Society for Information Science and Technology 58(7), 1019–1031 (2007)CrossRefGoogle Scholar
  8. 8.
    Kuhn, M.: Building predictive models in R using the caret package. Journal of Statistical Software 28(5), 1–26 (2008)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jina Lee
    • 1
    • 2
  • Kiran Lakkaraju
    • 1
    • 2
  1. 1.Sandia National LaboratoriesLivermoreUSA
  2. 2.Sandia National LaboratoriesAlbuquerqueUSA

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