The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Quality Control in Crowdsourcing: A Survey of Quality Attributes, Assessment Techniques, and Assurance Actions
Abstract
Crowdsourcing enables one to leverage on the intelligence and wisdom of potentially large groups of individuals toward solving problems. Common problems approached with crowdsourcing are labeling images, translating or transcribing text, providing opinions or ideas, and similar—all tasks that computers are not good at or where they may even fail altogether. The introduction of humans into computations and/or everyday work, however, also poses critical, novel challenges in terms of quality control, as the crowd is typically composed of people with unknown and very diverse abilities, skills, interests, personal objectives, and technological resources. This survey studies quality in the context of crowdsourcing along several dimensions, so as to define and characterize it and to understand the current state of the art. Specifically, this survey derives a quality model for crowdsourcing tasks, identifies the methods and techniques that can be used to assess the attributes of the model, and the actions and strategies that help prevent and mitigate quality problems. An analysis of how these features are supported by the state of the art further identifies open issues and informs an outlook on hot future research directions.
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Kittur, A., Nickerson, J. V., Bernstein, M., Gerber, E., Shaw, A., Zimmerman, J., Lease, M., & Horton, J. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441923 · Cited by 1,034 · cited 4×
“Quality control in crowdsourcing all revolves around achieving high-quality output data, which is the core challenge for mass adoption of crowd work (Kittur et al. 2013).”
InfluentialSoylent: a word processor with a crowd inside
Bernstein, M. S., Little, G., Miller, R. C., Hartmann, B., Ackerman, M. S., Karger, D. R., Crowell, D., & Panovich, K. (2010) · Proceedings of the 23nd annual ACM symposium on User interface software and technology · doi:10.1145/1866029.1866078 · Cited by 686
Shepherding the crowd yields better work
Dow, S., Kulkarni, A., Klemmer, S., & Hartmann, B. (2012) · Proceedings of the ACM 2012 conference on Computer Supported Cooperative Work · doi:10.1145/2145204.2145355 · Cited by 334
Viégas, F. B., Wattenberg, M., & McKeon, M. M. (2007) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-73257-0_49 · Cited by 195
Enhancing reliability using peer consistency evaluation in human computation
Huang, S.-W., & Fu, W.-T. (2013) · Proceedings of the 2013 conference on Computer supported cooperative work · doi:10.1145/2441776.2441847 · Cited by 50
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Perspectives on Large Language Models for Relevance Judgment
Faggioli, G., Dietz, L., Clarke, C. L. A., Demartini, G., Hagen, M., Hauff, C., Kando, N., Kanoulas, E., Potthast, M., Stein, B., et al. (2023) · Proceedings of the 2023 ACM SIGIR International Conference on Theory of Information Retrieval · doi:10.1145/3578337.3605136 · Cited by 184
Data Evaluation and Enhancement for Quality Improvement of Machine Learning
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Metadata
- Authors
- Daniel, F., Kucherbaev, P., Cappiello, C., Benatallah, B., & Allahbakhsh, M.
- Venue
- ACM Computing Surveys
- Published
- 2018-01-04
- Volume
- 51
- Issue
- 1
- Pages
- 1-40
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W4292341621
- Cited by
- 313
- References
- 5
- FWCI
- 48.87
- Type
- journal article
- Language
- en
- Open access
- closed
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