Computers can’t give credit: How automatic attribution falls short in an online remixing community
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 ef- fects of a technological design intervention introducing au- tomated 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 qualita- tive analysis of twelve in-depth, semi-structured, interviews with Scratch participants on the subject of remixing and at- tribution. 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.