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Crowdsourcing for Information Visualization: Promises and Pitfalls

  • Conference paper
  • First Online:
Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10264))

  • 1329 Accesses

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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Notes

  1. 1.

    http://www.wired.com/2006/06/crowds last accessed 14 Jun 2017.

  2. 2.

    We adopt this terminology, which means a single self-contained task, from Amazon Mechanical Turk.

  3. 3.

    https://medium.com/@silberman/stop-citing-ross-et-al-2010-who-are-the-crowdworkers-b3b9b1e8d300 last accessed 14 Jun 2017.

  4. 4.

    http://demographics.mturk-tracker.com last accessed 14 Jun 2017.

  5. 5.

    http://www.quizrevolution.com/act101820/mini/go/ last accessed 14 Jun 2017, http://perceptualedge.com/files/GraphDesignIQ.html last accessed 14 Jun 2017.

  6. 6.

    http://www.colourblindawareness.org/colour-blindness/types-of-colour-blindness last accessed 14 Jun 2017.

  7. 7.

    http://www.color-blindness.com/color-blindness-tests last accessed 14 Jun 2017.

  8. 8.

    https://fold.it/portal last accessed 14 Jun 2017.

  9. 9.

    https://github.com/codementum/experimentr last accessed 14 Jun 2017.

  10. 10.

    http://archive.ics.uci.edu/ml last accessed 14 Jun 2017.

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Author information

Authors and Affiliations

  1. King’s College London, London, UK

    Rita Borgo

  2. Microsoft Research, Redmond, USA

    Bongshin Lee

  3. Microsoft Research - Inria, Paris, France

    Benjamin Bach

  4. University of Zurich, Zurich, Switzerland

    Sara Fabrikant

  5. City University London, London, UK

    Radu Jianu

  6. Linnaeus University, Växjö, Sweden

    Andreas Kerren

  7. University of Arizona, Tucson, USA

    Stephen Kobourov

  8. Luxembourg Institute of Science and Technology, Esch-sur-Alzette, Luxembourg

    Fintan McGee

  9. Helsinki Institute for Information Technology, Aalto, Finland

    Luana Micallef

  10. Darmstadt University, Darmstadt, Germany

    Tatiana von Landesberger & Katrin Ballweg

  11. University Trier, Trier, Germany

    Stephan Diehl

  12. Juji, Saratoga, USA

    Michelle Zhou

  13. Swansea University, Swansea, UK

    Paolo Simonetto

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  1. Rita Borgo
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Correspondence to Rita Borgo .

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Editors and Affiliations

  1. Department of Computer Science, Swansea University, Swansea, United Kingdom

    Daniel Archambault

  2. University of Glasgow, Glasgow, United Kingdom

    Helen Purchase

  3. Modellierung adaptiver Systeme, Universität Duisburg-Essen, Essen, Germany

    Tobias Hoßfeld

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Borgo, R. et al. (2017). Crowdsourcing for Information Visualization: Promises and Pitfalls. In: Archambault, D., Purchase, H., Hoßfeld, T. (eds) Evaluation in the Crowd. Crowdsourcing and Human-Centered Experiments. Lecture Notes in Computer Science(), vol 10264. Springer, Cham. https://doi.org/10.1007/978-3-319-66435-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-66435-4_5

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  • Online ISBN: 978-3-319-66435-4

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