Computers Can't Give Credit: How Automatic Attribution Falls Short in an Online Remixing Community

TL;DR

This study investigates how automatic attribution in online remixing communities affects user reactions, finding that human-given credit holds more social value than automated systems, with implications for platform design.

Contribution

It provides a comparative analysis of automated versus manual attribution effects on user reactions in a large online remixing community.

Findings

Automatic attribution influences user reactions differently than manual credit.

Human-given credit carries more social significance than automated attribution.

Design implications for online communities and social media platforms are discussed.

Abstract

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.