观众和主播在Twitch上的规模参与
Audience and Streamer Participation at Scale on Twitch
摘要：
像Twitch这样的大规模流媒体平台越来越受欢迎，但详细的观众-流媒体互动动态仍然没有大规模探索。在本文中，我们对一个拥有超过1200万观众聊天信息和45小时流媒体视频的数据集进行了混合方法研究，以了解Twitch上的观众参与和流媒体性能。我们根据大小和观众参与风格揭示了五种类型的流媒体：Clique Streams，具有密切的流媒体与观众互动的小型流媒体; Rising Streamers，使用自定义技术和主持人来正式化其社区的中档流媒体; Chatter-boxes，具有既定会话动态的中档流媒体; Spotlight Streamers，吸引大量观众同时仍保留社区感的大型流媒体;和专业人士，大量的流与体育场风格的观众。我们讨论了每种风格的主播和观众所面临的挑战和机遇，并通过提供数据支持的设计影响来总结，这些设计影响赋予主播，观众，直播平台和游戏设计师权力
英文摘要：
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
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。
