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Visual Sensemaking of Massive Crowdsourced Data for Design Ideation

Part of: Design Creativity

Published online by Cambridge University Press:  26 July 2019

Yuejun He ,
Bradley Camburn ,
Jianxi Luo ,
Maria C. Yang  and
Kristin L. Wood
Yuejun He
Affiliation:
Singapore University of Technology and Design;
Bradley Camburn
Affiliation:
Singapore University of Technology and Design;
Jianxi Luo
Affiliation:
Singapore University of Technology and Design;
Maria C. Yang
Affiliation:
Massachusetts Institute of Technology
Kristin L. Wood
Affiliation:
Singapore University of Technology and Design;

Abstract

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Textual idea data from online crowdsourcing contains rich information of the concepts that underlie the original ideas and can be recombined to generate new ideas. But representing such information in a way that can stimulate new ideas is not a trivial task, because crowdsourced data are often vast and in unstructured natural languages. This paper introduces a method that uses natural language processing to summarize a massive number of idea descriptions and represents the underlying concept space as word clouds with a core-periphery structure to inspire recombinations of such concepts into new ideas. We report the use of this method in a real public-sector-sponsored project to explore ideas for future transportation system design. Word clouds that represent the concept space underlying original crowdsourced ideas are used as ideation aids and stimulate many new ideas with varied novelty, usefulness and feasibility. The new ideas suggest that the proposed method helps expand the idea space. Our analysis of these ideas and a survey with the designers who generated them shed light on how people perceive and use the word clouds as ideation aids and suggest future research directions.

Keywords

Design ideationCore-periphery structureCrowdsourcingCreativityComputer Aided Design (CAD)
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 409 - 418
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

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