The influence of crowd type and task complexity on crowdsourced work quality

Ria Mae Borromeo, Thomas Laurent, Motomichi Toyama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

As the use of crowdsourcing spreads, the need to ensure the quality of crowdsourced work is magnified. While quality control in crowdsourcing has been widely studied, established mechanisms may still be improved to take into account other factors that affect quality. However, since crowdsourcing relies on humans, it is difficult to identify and consider all factors affecting quality. In this study, we conduct an initial investigation on the effect of crowd type and task complexity on work quality by crowdsourcing a simple and more complex version of a data extraction task to paid and unpaid crowds. We then measure the quality of the results in terms of its similarity to a gold standard data set. Our experiments show that the unpaid crowd produces results of high quality regardless of the type of task while the paid crowd yields better results in simple tasks. We intend to extend our work to integrate existing quality con-trol mechanisms and perform more experiments with more varied crowd members.

Original languageEnglish
Title of host publicationProceedings of the 20th International Database Engineering and Applications Symposium, IDEAS 2016
PublisherAssociation for Computing Machinery
Pages70-76
Number of pages7
Volume11-13-July-2016
ISBN (Electronic)9781450341189
DOIs
Publication statusPublished - 2016 Jul 11
Event20th International Database Engineering and Applications Symposium, IDEAS 2016 - Montreal, Canada
Duration: 2016 Jul 112016 Jul 13

Other

Other20th International Database Engineering and Applications Symposium, IDEAS 2016
CountryCanada
CityMontreal
Period16/7/1116/7/13

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Keywords

  • Crowdsourcing
  • Task complexity
  • Text extraction

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Borromeo, R. M., Laurent, T., & Toyama, M. (2016). The influence of crowd type and task complexity on crowdsourced work quality. In Proceedings of the 20th International Database Engineering and Applications Symposium, IDEAS 2016 (Vol. 11-13-July-2016, pp. 70-76). Association for Computing Machinery. https://doi.org/10.1145/2938503.2938511