Borgo, Rita and Lee, Bongshin and Bach, Benjamin and Fabrikant, Sara and Jianu, Radu and Kerren, Andreas and Kobourov, Stephen and McGee, Fintan and Micallef, Luana and Landesberger, Tatiana von and Ballweg, Kathrin and Diehl, Stephan and Simonetto, Paolo and Zhou, Michelle (2017):
Crowdsourcing for Information Visualization: Promises and Pitfalls.
In: Evaluation in the Crowd, pp. 96-138,
Berlin, Springer, 1581. Dagstuhl-Seminar, Dagstuhl Castle, Germany, November 22 – 27, 2015, ISBN Print 978-3-319-66434-7 Online ISBN 978-3-319-66435-4,
DOI: 10.1007/978-3-319-66435-4_5,
[Conference or Workshop Item]

Abstract

Crowdsourcing offers great potential to overcome the limitations of controlled lab studies. To guide future designs of crowdsourcing-based studies for visualization, we review visualization research that has attempted to leverage crowdsourcing for empirical evaluations of visualizations. We discuss six core aspects for successful employment of crowdsourcing in empirical studies for visualization - participants, study design, study procedure, data, tasks, and metrics & measures. We then present four case studies, discussing potential mechanisms to overcome common pitfalls. This chapter will help the visualization community understand how to effectively and efficiently take advantage of the exciting potential crowdsourcing has to offer to support empirical visualization research.

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