通过观察者的眼睛进行即时参与预测：Twitch上的PUBG流媒体
Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on Twitch
摘要：
是否有可能仅仅基于游戏遥测来预测每时每刻的游戏参与度？我们能否通过观察游戏观众的行为方式来揭示游戏中引人入胜的时刻？为了在本文中解决这些问题，我们重新定义了游戏参与的定义方式，并通过游戏现场观众的眼睛来看待它。我们根据从Twitch流媒体服务获得的流行战斗royale游戏PlayerUnknown's Battlegrounds收集的数据构建观众参与度的预测模型。特别是，我们收集了观众的聊天记录和游戏中的遥测数据，这些数据来自五个流行流媒体的数百场比赛（包含超过100，000个游戏事件），并使用小型神经网络架构，在游戏过程中机器学习游戏玩法和观众聊天频率之间的映射。我们的主要研究结果表明，仅在40个游戏功能上训练的参与模型平均可以达到80%的准确率，最高可以达到84%。我们的模型是可扩展和可推广的，因为它们在流媒体内和跨流媒体播放风格以及跨流媒体播放风格中表现同样出色。
英文摘要：
Is it possible to predict moment-to-moment gameplay engagement based solely on game telemetry? Can we reveal engaging moments of gameplay by observing the way the viewers of the game behave? To address these questions in this paper, we reframe the way gameplay engagement is defined and we view it, instead, through the eyes of a game's live audience. We build prediction models for viewers' engagement based on data collected from the popular battle royale game PlayerUnknown's Battlegrounds as obtained from the Twitch streaming service. In particular, we collect viewers' chat logs and in-game telemetry data from several hundred matches of five popular streamers (containing over 100,000 game events) and machine learn the mapping between gameplay and viewer chat frequency during play, using small neural network architectures. Our key findings showcase that engagement models trained solely on 40 gameplay features can reach accuracies of up to 80% on average and 84% at best. Our models are scalable and generalisable as they perform equally well within- and across-streamers, as well as across streamer play styles.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
