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Studying toxic behavior influence and player chat in an online video game

Published:23 August 2017Publication History

ABSTRACT

Many online collaborative games, e-sports in particular, heavily rely on teamwork. However, players can act in an antisocial way during the match, creating dissent into the match. This kind of behavior is referred to as toxic. We aim to discover the influence brought by toxic behavior in a popular e-sport, League of Legends, through the study of communication patterns of players during the match. We discovered that different communication patterns exist, and that they are directly related to player performance and level of toxic behavior. We also propose metrics to analyze players' performance and the toxic contamination level, which measures the negative impacts of the toxic behavior. Our analysis contributes to shed light on how players behave in an online game, and opens ways to provide a better ambience on the online video game community.

References

  1. Charu C Aggarwal and ChengXiang Zhai. 2012. A Survey of Text Clustering Algorithms. In Mining text data. Springer, 77--128.Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Jane Barnett, Mark Coulson, and Nigel Foreman. 2010. Examining Player Anger in World of Warcraft. Human-Computer Interaction (2010), 147--160.Google ScholarGoogle Scholar
  3. Steven Bird, Ewan Klein, and Edward Loper. 2009. Natural language processing with Python: analyzing text with the natural language toolkit. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Jeremy Blackburn and Haewoon Kwak. 2014. STFU NOOB!. In Proceedings of the 23rd international conference on World Wide Web - WWW '14. 877--888. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. 2003. Latent Dirichlet Allocation. Journal of machine Learning research 3 (2003), 993--1022. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Rodger Caudill. 2015. Altruism Online : An Ethnographic Exploration into League of Legends. Summer Research (2015).Google ScholarGoogle Scholar
  7. Vivian Hsueh-Hua Chen, Henry Been-Lirn Duh, and Chiew Woon Ng. 2009. Players who Play to Make Others Cry. In International Conference on Advances in Computer Enterntainment Technology. 341--344. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. Chek Yang Foo and Elina M. I. Koivisto. 2004. Defining Grief Play in MMORPGs. In Proceedings of the 2004 ACM SIGCHI International Conference on Advances in computer entertainment technology - ACE '04. 245--250. Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Haewoon Kwak and Jeremy Blackburn. 2014. Linguistic Analysis of Toxic Behavior in an Online Video Game. EGG workshop Cmc (2014), 209--217.Google ScholarGoogle Scholar
  10. Quoc V. Le and Tomas Mikolov. 2014. Distributed Representations of Sentences and Documents.. In ICML, Vol. 14. 1188--1196. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Holin Lin and Sun Chuen-Tsai. 2005. The `White-eyed' Player Culture: Grief Play and Construction of Deviance in MMORPGs. In DiGRA 2005 Conference: Changing Views - Worlds in Play.Google ScholarGoogle Scholar
  12. J. Lin. 2015. More Science Behind Shaping Player Behavior in Online Game. In Game Developers Conference 2015.Google ScholarGoogle Scholar
  13. Radim Rehůřek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora. In Proceedings of the LREC 2010 Workshop on New Challenges for NLP Frameworks. 45--50.Google ScholarGoogle Scholar
  14. Kenneth B. Shores, Yilin He, Kristina L. Swanenburg, Robert Kraut, and John Riedl. 2014. The Identification of Deviance and its Impact on Retention in a Multiplayer Game. Proceedings of the 17th ACM conference on Computer Supported Cooperative Work & Social Computing - CSCW '14 (2014), 1356--1365. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. John Suler. 2004. The Online Disinhibition Effect. CyberPsychology & Behavior 7, 3 (2004), 321--326.Google ScholarGoogle Scholar

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  • Published in

    cover image ACM Conferences
    WI '17: Proceedings of the International Conference on Web Intelligence
    August 2017
    1284 pages
    ISBN:9781450349512
    DOI:10.1145/3106426

    Copyright © 2017 ACM

    Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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    Association for Computing Machinery

    New York, NY, United States

    Publication History

    • Published: 23 August 2017

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    WI '17 Paper Acceptance Rate118of178submissions,66%Overall Acceptance Rate118of178submissions,66%

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