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Predicting guild membership in massively multiplayer online games (2014)
| Venue: | in SBP |
| Citations: | 2 - 2 self |
BibTeX
@INPROCEEDINGS{Alvari14predictingguild,
author = {Hamidreza Alvari and Kiran Lakkaraju and Gita Sukthankar and Jon Whetzel},
title = {Predicting guild membership in massively multiplayer online games},
booktitle = {in SBP},
year = {2014}
}
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Abstract
Abstract. Massively multiplayer online games (MMOGs) offer a unique laboratory for examining large-scale patterns of human behavior. In par-ticular, the study of guilds in MMOGs has yielded insights about the forces driving the formation of human groups. In this paper, we present a computational model for predicting guild membership in MMOGs and evaluate the relative contribution of 1) social ties, 2) attribute homophily, and 3) existing guild membership toward the accuracy of the predictive model. Our results indicate that existing guild membership is the best predictor of future membership; moreover knowing the identity of a few influential members, as measured by network centrality, is a more power-ful predictor than a larger number of less influential members. Based on these results, we propose that community detection algorithms for virtual worlds should exploit publicly available knowledge of guild membership from sources such as profiles, bulletin boards, and chat groups.
Keyphrases
guild membership multiplayer online game influential member community detection algorithm unique laboratory social tie human behavior large-scale pattern relative contribution human group power-ful predictor predictive model future membership virtual world available knowledge massively multiplayer online game computational model bulletin board network centrality
