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Crowdsourcing and Crowd Work

Published: 06 May 2017 Publication History

Abstract

Crowdsourcing and human computation are useful in a number of real-world applications. Crowds generate large data sets useful for natural language processing and computer vision; they work together to formulate intelligent responses far beyond what we can automate; and they power intelligent interactive systems currently impossible with automated approaches alone. In this course, affendees will learn how to work with the crowd to enable research and practical applications. They will gain experience from the worker's perspective, receive an introduction to writing programs that work with existing sources of crowds, (e.g., Amazon Mechanical Turk), apply usability principles for designing crowd tasks that elicit high-quality responses, use statistical methods to improve the quality of the work received, build systems that interface with crowd labor in real time, and conduct experiments to improve understanding of the differences between different sources of crowd work. The course will provide hands-on activities on each of these topics, and provide pointers to material that can provide more in depth information.

References

[1]
Jeffrey P. Bigham, Chandrika Jayant, Hanjie Ji, Greg Little, Andrew Miller, Robert C. Miller, Robin Miller, Aubrey Tatarowicz, Brandyn White, Samual White, and Tom Yeh. 2010. VizWiz: Nearly Real-time Answers to Visual Questions. In Proceedings of the 23Nd Annual ACM Symposium on User Interface Software and Technology (UIST '10). ACM, New York, NY, USA, 333--342.
[2]
Walter S. Lasecki, Mitchell Gordon, Danai Koutra, Malte F. Jung, Steven P. Dow, and Jeffrey P. Bigham. 2014. Glance: Rapidly Coding Behavioral Video with the Crowd. In Proceedings of the 27th Annual ACM Symposium on User Interface Software and Technology (UIST '14). ACM, New York, NY, USA, 551--562.
[3]
Walter S. Lasecki, Chris Homan, and Jeffrey P. Bigham. 2014. Architecting Real-Time CrowdPowered Systems. Human Computation Journal (September 2014).
[4]
Walter S. Lasecki, Kyle I. Murray, Samuel White, Robert C. Miller, and Jeffrey P. Bigham. 2011. Real-time Crowd Control of Existing Interfaces. In Proceedings of the 24th Annual ACM Symposium on User Interface Software and Technology (UIST '11). ACM, New York, NY, USA, 23--32.
[5]
Walter S. Lasecki, Rachel Wesley, Jeffrey Nichols, Anand Kulkarni, James F. Allen, and Jeffrey P. Bigham. 2013. Chorus: A Crowd-powered Conversational Assistant. In Proceedings of the 26th Annual ACM Symposium on User Interface Software and Technology (UIST '13). ACM, New York, NY, USA, 151--162.

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    cover image ACM Conferences
    CHI EA '17: Proceedings of the 2017 CHI Conference Extended Abstracts on Human Factors in Computing Systems
    May 2017
    3954 pages
    ISBN:9781450346566
    DOI:10.1145/3027063
    Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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    New York, NY, United States

    Publication History

    Published: 06 May 2017

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    1. crowd work
    2. crowdsourcing

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    CHI EA '17 Paper Acceptance Rate 1,000 of 5,000 submissions, 20%;
    Overall Acceptance Rate 6,164 of 23,696 submissions, 26%

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    CHI 2025
    ACM CHI Conference on Human Factors in Computing Systems
    April 26 - May 1, 2025
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