D. Oliveira, R. Silva Ferreira, et al.
EAGE/PESGB Workshop Machine Learning 2018
In spite of decades of research on virtual worlds, our understanding of one popular form of virtual world behavior - raiding - remains limited. Raiding is important because it entails intense, high-risk, and complex collaborative behaviors in computer-mediated environments. This paper contributes to CSCW literature by offering a longitudinal analysis of raiding behavior using system data manually collected from the game world itself, comparing two raiding teams as they worked through the same content. Supplemented with interviews and chat transcripts, this research sheds light on what actually happens during raids across four different temporal scales: seconds, hours, days, and months. It also distinguishes between behaviors that are imposed by the system design and those chosen by players. Finally, it derives two viable raiding styles from the data. © 2012 ACM.
D. Oliveira, R. Silva Ferreira, et al.
EAGE/PESGB Workshop Machine Learning 2018
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SYSTOR 2011
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