Crowdsourcing for Information Visualization: Promises and Pitfalls
Crowdsourcing for Information Visualization: Promises and Pitfalls
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DOI：
10.1007/978-3-319-66435-4_5
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
R. Borgo;Bongshin Lee;Benjamin Bach;S. Fabrikant;R. Jianu;A. Kerren;S. Kobourov;F. McGee;L. Micallef;T. V. Landesberger;K. Ballweg;S. Diehl;Paolo Simonetto;Michelle X. Zhou
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文献类型：
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作者：
R. Borgo;Bongshin Lee;Benjamin Bach;S. Fabrikant;R. Jianu;A. Kerren;S. Kobourov;F. McGee;L. Micallef;T. V. Landesberger;K. Ballweg;S. Diehl;Paolo Simonetto;Michelle X. Zhou
众包为克服受控实验室研究的局限性提供了巨大的潜力。为了指导未来的设计基于众包的可视化研究，我们回顾可视化研究，试图利用众包可视化的实证评估。我们讨论了六个核心方面的成功就业的众包可视化实证研究-参与者，研究设计，研究程序，数据，任务，指标和措施。然后，我们提出了四个案例研究，讨论了克服常见陷阱的潜在机制。本章将帮助可视化社区了解如何有效和高效地利用众包提供的令人兴奋的潜力来支持实证可视化研究。
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.