Large-scale streaming platforms such as Twitch are becoming increasingly popular, but detailed audience-streamer interaction dynamics remain unexplored at scale. In this paper, we perform a mixed-methods study on a dataset with over 12 million audience chat messages and 45 hours of streaming video to understand audience participation and streamer performance on Twitch. We uncover five types of streams based on size and audience participation styles: Clique Streams, small streams with close streamer-audience interactions; Rising Streamers, mid-range streams using custom technology and moderators to formalize their communities; Chatter-boxes, mid-range streams with established conversational dynamics; Spotlight Streamers, large streams that engage large numbers of viewers while still retaining a sense of community; and Professionals, massive streams with the stadium-style audiences. We discuss challenges and opportunities emerging for streamers and audiences from each style and conclude by providing data-backed design implications that empower streamers, audiences, live streaming platforms, and game designers
翻译:大型流流平台,如Twitch等大型流平台越来越受欢迎,但详细的观众-流流互动动态尚未大规模探索。 在本文中,我们对数据集进行混合方法研究,由超过1 200万观众聊天讯息和45小时流视频组成,以了解观众参与情况以及Twitch的流动性能。我们根据参与规模和观众参与方式发现了五类流流:Clique Streams,小溪流,与近流-观众互动;使用定制技术使流流、中流流流和主持人使社区正规化; 聊天盒,有固定对话动态的中流; Spotter-box,有大量观众参与的大流流,同时保持社区感; 以及专业人员,体育场式受众的大规模流。 我们讨论流者和来自每种风格的受众新出现的挑战和机遇,并通过提供数据支持的设计影响,增强流者、受众、现场流平台和游戏设计者的能力。