论文标题
通过观察者的眼睛的眼光进行瞬间的参与预测：Twitch上的PUBG流媒体
Moment-to-moment Engagement Prediction through the Eyes of the Observer: PUBG Streaming on Twitch
论文作者
论文摘要
是否可以仅根据游戏遥测来预测时刻的游戏玩法？我们可以通过观察游戏的观众表现方式来揭示游戏玩法的吸引力吗？为了在本文中解决这些问题，我们重新构架了游戏玩法的定义方式，而是通过游戏现场观众的眼光来查看它。我们根据从Twitch流媒体服务中获得的流行的Battle Royale Game Playerunknown的战场收集的数据建立了观众参与度的预测模型。特别是，我们从五个受欢迎的流媒体（包含100,000多个游戏事件）的数百场比赛中收集观众的聊天日志和游戏中的遥测数据，并使用小型神经网络体系结构学习游戏玩法和观众聊天频率之间的映射。我们的主要发现表明，仅在40个游戏功能上接受培训的参与模型可以平均达到80％的准确性，最多可以达到84％。我们的模型可扩展且可概括，因为它们在跨流和跨流式播放样式的内部和跨流相同。
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.
