Predicting MMO Player Gender from In-Game Attributes Using Machine Learning Models

Tracy Kennedy, Rabindra (Robby) Ratan, Komal Kapoor, Nishith Pathak, Dmitri Williams, Jaideep Srivastava

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

What in-game attributes predict players’ offline gender? Our research addresses this question using behavioral logs of over 4,000 EverQuest II players. The analysis compares four variable sets with multiple combinations of character types (avatar characteristics or gameplay behaviors; primary or nonprimary character), three server types within the game (roleplaying, player-vs-player, and player-vs-environment), and three types of predictive machine learning models (JRip, J48, and Random Tree). Overall, the most highly predictive, interpretable model has an f-measure of 0.94 and suggests the primary character gender and number of male and female characters a player has provide the most prediction value, with players choosing characters to match their own gender. The results also suggest that female players craft, scribe recipes, and harvest items more than male players. While the strength of these findings varies by server type, they are generally consistent with previous research and suggest that players tend to play in ways that are consistent with their offline identities.

Original languageEnglish
Title of host publicationSpringer Proceedings in Complexity
PublisherSpringer
Pages69-84
Number of pages16
VolumePart F3
DOIs
Publication statusPublished - 1 Jan 2014
Externally publishedYes

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Keywords

  • Gender in MMOs
  • Gender prediction
  • Machine learning models
  • MMOs

ASJC Scopus subject areas

  • Applied Mathematics
  • Modelling and Simulation
  • Computer Science Applications

Cite this

Kennedy, T., Ratan, R. R., Kapoor, K., Pathak, N., Williams, D., & Srivastava, J. (2014). Predicting MMO Player Gender from In-Game Attributes Using Machine Learning Models. In Springer Proceedings in Complexity (Vol. Part F3, pp. 69-84). Springer. https://doi.org/10.1007/978-3-319-07142-8_5