Computers Can't Give Credit: How Automatic Attribution Falls Short in an Online Remixing Community
This addresses a problem for designers of online communities and social media platforms, highlighting the limitations of automated systems in fostering meaningful user interactions, and is incremental in building on prior work about attribution in digital spaces.
The paper investigates how automatic attribution affects user reactions to content remixing in the Scratch online community, finding that automated systems provide less valuable credit compared to manual attribution by humans, despite presenting similar information.
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.