Crowdsourcing for information visualization: Promises and pitfalls

Rita Borgo, Bongshin Lee, Benjamin Bach, Sara Fabrikant, Radu Jianu, Andreas Kerren, Stephen G Kobourov, Fintan McGee, Luana Micallef, Tatiana von Landesberger, Katrin Ballweg, Stephan Diehl, Paolo Simonetto, Michelle Zhou

Research output: Chapter in Book/Report/Conference proceedingChapter

7 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages96-138
Number of pages43
Volume10264 LNCS
DOIs
StatePublished - 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10264 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Crowdsourcing for information visualization: Promises and pitfalls'. Together they form a unique fingerprint.

  • Cite this

    Borgo, R., Lee, B., Bach, B., Fabrikant, S., Jianu, R., Kerren, A., Kobourov, S. G., McGee, F., Micallef, L., von Landesberger, T., Ballweg, K., Diehl, S., Simonetto, P., & Zhou, M. (2017). Crowdsourcing for information visualization: Promises and pitfalls. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10264 LNCS, pp. 96-138). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10264 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66435-4_5