Team communication plays a vital role in supporting collaboration in multiplayer online games. Therefore, numerous studies were conducted to examine communication patterns in esports teams. While non-verbal communication has been extensively investigated, research on assessing voice-based verbal communication patterns remains relatively understudied. In this study, we propose a framework that automatically assesses verbal communication patterns by constructing networks with utterances transcribed from voice recordings. Through a data collection study, we obtained 84 game sessions from five League of Legends teams and subsequently investigated how verbal communication patterns varied across different conditions. As a result, we revealed that esports players exhibited broader and more balanced participation in collaborative situations, increased utterances over time with the largest rise in decision making, and team-level differences that were contingent on effective professional training. Building upon these findings, this study provides a generalizable tool for analyzing effective team communication.
翻译:团队交流在支持多人在线游戏协作中起着至关重要的作用。因此,已有大量研究探讨电竞团队中的交流模式。尽管非语言交流已得到广泛研究,但针对基于语音的言语交流模式评估的研究仍相对不足。本研究提出一个框架,通过利用语音记录转写的语句构建网络,自动评估言语交流模式。通过一项数据收集研究,我们从五个《英雄联盟》战队获取了84场游戏对局数据,进而探究了言语交流模式在不同条件下的变化。结果表明,电竞选手在协作情境中表现出更广泛且更均衡的参与度,其话语量随时间增长,其中决策相关语句增幅最大,并且团队层面的差异取决于有效的专业训练。基于这些发现,本研究为分析高效团队交流提供了一个可推广的工具。