Cited By
View all- Hindalong EJohnson JCarenini GMunzner T(2022)Abstractions for Visualizing Preferences in Group DecisionsProceedings of the ACM on Human-Computer Interaction10.1145/35128966:CSCW1(1-44)Online publication date: 7-Apr-2022
Scalable Crowd Ideation Support through Data Visualization, Mining, and Structured Workflows
Pages 183 - 186
Abstract
As the size of innovation communities increases, methods of supporting their creativity need to scale as well. Our research proposes the integration of three scalable techniques into a crowd ideation system: 1) data visualization, 2) structured microtask workflows, and 3) data mining, with the goal of supporting users in convergent and divergent ideation processes. In addition, these techniques do not work in isolation, but instead support each other. Our vision is to create a system that intelligently supports users' ideation in a crowd context while maintaining their agency and facilitating exploration and decision-making.
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Index Terms
- Scalable Crowd Ideation Support through Data Visualization, Mining, and Structured Workflows
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Published In
February 2017
472 pages
ISBN:9781450346887
DOI:10.1145/3022198
- General Chairs:
- Charlotte P. Lee,
- Steve Poltrock,
- Program Chairs:
- Louise Barkhuus,
- Marcos Borges,
- Wendy Kellogg
Copyright © 2017 Owner/Author.
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Published: 25 February 2017
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CSCW '17: Computer Supported Cooperative Work and Social Computing
February 25 - March 1, 2017
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CSCW '17 Companion Paper Acceptance Rate 183 of 530 submissions, 35%;
Overall Acceptance Rate 2,235 of 8,521 submissions, 26%
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View all- Hindalong EJohnson JCarenini GMunzner T(2022)Abstractions for Visualizing Preferences in Group DecisionsProceedings of the ACM on Human-Computer Interaction10.1145/35128966:CSCW1(1-44)Online publication date: 7-Apr-2022
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