论文标题
观众和流媒体在抽搐时大规模参与
Audience and Streamer Participation at Scale on Twitch
论文作者
论文摘要
大规模的流媒体平台（例如Twitch）变得越来越流行，但是详细的受众群交互动力学仍未大规模探索。在本文中，我们在数据集上进行了一项混合方法研究，其中包含超过1200万个观众聊天消息和45小时的流式视频，以了解受众的参与和Twitch上的流媒体表现。我们根据大小和受众参与方式发现了五种类型的流：集团流，具有紧密流媒体交互的小流；使用自定义技术和主持人正式化其社区的流长仪，中端流； chat不休，具有既定的对话动力学的中端流；聚光灯流媒体，大量观众，同时仍保持社区意识；以及专业人士，大量的溪流与体育场风格的观众。我们讨论了每种样式的流媒体和观众出现的挑战和机会，并通过提供数据支持的设计含义来结束，以增强流媒体，受众，实时流媒体平台和游戏设计师的能力
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
