Crowdsourcing for information visualization: Promises and pitfalls

Auteurs

R. Borgo, B. Lee, B. Bach, S. Fabrikant, R. Jianu, A. Kerren, S. Kobourov, F.  McGee, L. Micallef, T. von Landesberger, K. Ballweg, S. Diehl, P. Simonetto, and M. Zhou

Référence

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10264 LNCS, pp. 96-138, 2017

Description

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.

Lien

doi:10.1007/978-3-319-66435-4_5