Mechanical Novel: Crowdsourcing Complex Work Through Reflection and Revision

Joy Kim (), Sarah Sterman (), Allegra Argent Beal Cohen () and Michael S. Bernstein ()
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Joy Kim: Stanford University
Sarah Sterman: Stanford University
Allegra Argent Beal Cohen: Stanford University
Michael S. Bernstein: Stanford University

A chapter in Design Thinking Research, 2018, pp 79-104 from Springer

Abstract: Abstract Crowdsourcing systems accomplish large tasks with scale and speed by breaking work down into independent parts. However, many types of complex creative work, such as fiction writing, have remained out of reach for crowds because work is tightly interdependent: changing one part of a story may trigger changes to the overall plot and vice versa. Taking inspiration from how expert authors write, we propose a technique for achieving interdependent complex goals with crowds. With this technique, the crowd loops between reflection, to select a high-level goal, and revision, to decompose that goal into low-level, actionable tasks. We embody this approach in Mechanical Novel, a system that crowdsources short fiction stories on Amazon Mechanical Turk. In a field experiment, Mechanical Novel resulted in higher-quality stories than an iterative crowdsourcing workflow. Our findings suggest that orienting crowd work around high-level goals may enable workers to coordinate their effort to accomplish complex work.

Date: 2018
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DOI: 10.1007/978-3-319-60967-6_5

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