TY - GEN
T1 - Computers can't give credit
T2 - How automatic attribution falls short in an online remixing community
AU - Monroy-Hernandez, Andres
AU - Hill, Benjamin Mako
AU - Gonzalez-Rivero, Jazmin
AU - Boyd, Danah
PY - 2011
Y1 - 2011
N2 - 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.
AB - 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.
KW - Attribution
KW - Credit-giving
KW - Online communities
KW - Remixing
KW - User-generated content
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U2 - 10.1145/1978942.1979452
DO - 10.1145/1978942.1979452
M3 - Conference contribution
AN - SCOPUS:79958142718
SN - 9781450302289
T3 - Conference on Human Factors in Computing Systems - Proceedings
SP - 3421
EP - 3430
BT - CHI 2011 - 29th Annual CHI Conference on Human Factors in Computing Systems, Conference Proceedings and Extended Abstracts
PB - Association for Computing Machinery
ER -