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Do Influencers Influence? – Analyzing Players’ Activity in an Online Multiplayer Game

机译:有影响力的人会影响吗? –在在线多人游戏中分析玩家的活动

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In social and online media, influencers have traditionally been understood as highly visible individuals. Recent outcomes suggest that people are likely to mimic influencers’ behavior, which can be exploited, for instance, in marketing strategies. Also in the Games User Research field, the interest in studying player social networks has emerged due to the heavy reliance on online influencers in marketing campaigns for games, as well as in keeping players engaged. Despite the inherent value of those individuals, it is still difficult to identify influencers, as the definition of influencers is a debated topic. Thus, how can we identify influencers, and are they indeed the individuals impacting others’ behavior? In this work, we focus on influence in retention to verify whether central players impacted others’ permanence in the game. We identified the central players in the social network built from the competitive player-vs-player (PvP) multiplayer (Crucible) matches in the online shooter Destiny. Then, we computed influence scores for each player evaluating the increase in similarity over time between two connected individuals. In this paper, we were able to show the first indications that the traditional metrics for influencers do not necessarily apply for games. On the contrary, we found that the group of central players was distinct from the group of influential players, defined as the individuals with the highest influence scores. Then, we provide an analysis of the two groups.
机译:在社会和在线媒体中,影响者传统上被理解为高度可见的个体。最近的结果表明,人们可能会模仿影响者的行为,这可以被利用,例如营销策略。同样在游戏用户研究领域,由于在线影响者在营销活动中的营销活动中的繁重依赖以及让玩家携手合作,因此出现了研究玩家社交网络的兴趣。尽管这些人的固有价值,但仍然难以识别影响者,因为影响者的定义是辩论的话题。因此,我们如何识别影响者,并且他们确实是影响他人的行为的人?在这项工作中,我们专注于保留的影响,以验证中央球员是否影响其他人在比赛中的持久性。我们在网上射击命运中的竞争球员-VS-Player(PVP)Multiplayer(PVP)Multiplyer(Crucible)匹配中建立了社交网络中的中央参与者。然后,我们计算每个玩家的影响分数,评估两个连接的人之间的相似性的增加。在本文中,我们能够展示第一个迹象表明,传统的影响因素的传统指标不一定适用于游戏。相反,我们发现,集团中央球员与一群有影响力的球员不同,定义为影响分数最高的个人。然后,我们提供了两组的分析。

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