计算机无法给予信用：在在线混剪社区中自动归因的局限性
Computers Can't Give Credit: How Automatic Attribution Falls Short in an Online Remixing Community
AI总结：
本文研究了在线混剪社区中自动归因的局限性，发现由人类提供的归因比科技系统自动归因更有效。
摘要：
在本文中，我们探讨了归因在塑造用户对内容重用或混剪在大型用户生成内容社区中的反应中的作用。我们使用来自Scratch在线社区的数据进行了两项研究——这是一个社交媒体平台，数以万计的年轻人分享和混剪动画和视频游戏。首先，我们进行了一项定量分析，研究了引入自动归因混剪的科技设计干预对用户因被混剪而反应的影响。我们将此分析与对“手动”归因的平行研究进行比较。其次，我们对十二次深入的半结构化访谈进行了定性分析，采访Scratch参与者讨论混剪和归因问题。两项研究的结果表明，由科技系统（即列出贡献者的姓名）执行的自动归因在作用上不同于，且不如由人类混剪者提供的归因重要。尽管表面上的信息相同，但当由人类混剪者提供时，归因会获得新的意义。我们讨论了这些发现对在线社区和社会媒体平台设计者的影响。
英文摘要：
In this paper, we explore the role that attribution plays in shaping user reactions to content reuse, or remixing, in a large user-generated content community. We present two studies using data from the Scratch online community -- a social media platform where hundreds of thousands of young people share and remix animations and video games. First, we present a quantitative analysis that examines the effects of a technological design intervention introducing automated attribution of remixes on users' reactions to being remixed. We compare this analysis to a parallel examination of "manual" credit-giving. Second, we present a qualitative analysis of twelve in-depth, semi-structured, interviews with Scratch participants on the subject of remixing and attribution. Results from both studies suggest that automatic attribution done by technological systems (i.e., the listing of names of contributors) plays a role that is distinct from, and less valuable than, credit which may superficially involve identical information but takes on new meaning when it is given by a human remixer. We discuss the implications of these findings for the designers of online communities and social media platforms.
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。
