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Crowdsourcing for Information Visualization: Promises and Pitfalls

Research output: Chapter in Book/Report/Conference proceedingChapter

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

Original languageEnglish
Title of host publicationEvaluation in the Crowd. Crowdsourcing and Human-Centered Experiments
Subtitle of host publicationDagstuhl Seminar 15481
EditorsDaniel Archambault, Helen Purchase, Tobias Hoßfeld
PublisherSpringer
Pages96-138
Number of pages4
ISBN (Electronic)978-3-319-66435-4
ISBN (Print)978-3-319-66434-7
DOIs
Publication statusE-pub ahead of print - 28 Sep 2017
EventDagstuhl Seminar 15481 - Dagstuhl Castle, Dagstuhl , Germany
Duration: 22 Nov 201527 Nov 2015
http://www.dagstuhl.de/de/programm/kalender/semhp/?semnr=15481

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10264
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349
Name Information Systems and Applications, incl. Internet/Web, and HCI
PublisherSpringer
Number10264

Conference

ConferenceDagstuhl Seminar 15481
CountryGermany
CityDagstuhl
Period22/11/201527/11/2015
Internet address

Documents

King's Authors

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

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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