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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

References

Arthur, W.B. (2007), “The structure of invention”. Research policy, Vol. 36, pp. 274–287.10.1016/j.respol.2006.11.005Google Scholar
Bird, S. and Loper, E. (2004), “NLTK: the natural language toolkit”. Proceedings of the ACL 2004 on Interactive poster and demonstration sessions, Association for Computational Linguistics, 31.Google Scholar
Borgatti, S.P. and Everett, M.G. (2000), “Models of core/periphery structures”. Social networks, Vol. 21, pp. 375–395.Google Scholar
Brabham, D.C. (2009), “Crowdsourcing the public participation process for planning projects”. Planning Theory, Vol. 8, pp. 242–262.10.1177/1473095209104824Google Scholar
Buzan, T. and Buzan, B. (1996), “The mind map book: How to use radiant thinking to maximize your brain's untapped potential”, Plume New York.Google Scholar
Chiu, I. and Shu, L. (2012), “Investigating effects of oppositely related semantic stimuli on design concept creativity”. Journal of Engineering Design, Vol. 23, pp. 271–296.10.1080/09544828.2011.603298Google Scholar
Comrey, A.L. (1962), “The minimum residual method of factor analysis”. Psychological Reports, Vol. 11, pp. 15–18.Google Scholar
Derczynski, L., Maynard, D., Rizzo, G., Van Erp, M., Gorrell, G., Troncy, R., Petrak, J. and Bontcheva, K. (2015), “Analysis of named entity recognition and linking for tweets”. Info. Processing & Management, Vol. 51, pp. 32–49.Google Scholar
Eberle, B. (1996), Scamper on: Games for imagination development, Prufrock Press Inc.Google Scholar
Fellows, I. (2014), “Wordcloud: Word Clouds (2014)”. R package version, Vol. 2.Google Scholar
Fleming, L. (2007), “Breakthroughs and the “long tail” of innovation”. MIT Sloan Management Review, Vol. 49, p. 69.Google Scholar
Fu, K., Cagan, J., Kotovsky, K. and Wood, K. (2013), “Discovering structure in design databases through functional and surface based mapping”. Journal of Mechanical Design, Vol. 135, p. 031006.10.1115/1.4023484Google Scholar
Goldschmidt, G. and Sever, A.L. (2011), “Inspiring design ideas with texts”. Design Studies, Vol. 32, pp. 139–155.Google Scholar
Gonçalves, M., Cardoso, C. and Badke-Schaub, P. (2014), “What inspires designers? Preferences on inspirational approaches during idea generation”. Design Studies, Vol. 35, pp. 29–53.10.1016/j.destud.2013.09.001Google Scholar
Goucher-Lambert, K. and Cagan, J. (2019), “Crowdsourcing inspiration: Using crowd generated inspirational stimuli to support designer ideation”. Design Studies, Vol. 61, pp. 1–29.10.1016/j.destud.2019.01.001Google Scholar
Goyal, A., Gupta, V. and Kumar, M. (2018), “Recent Named Entity Recognition and Classification techniques: A systematic review”. Computer Science Review, Vol. 29, pp. 21–43.10.1016/j.cosrev.2018.06.001Google Scholar
Grace, K., Maher, M.L., Preece, J., Yeh, T., Stangle, A. and Boston, C. (2015), “A process model for crowdsourcing design: A case study in citizen science”. Design Computing and Cognition'14. Springer.Google Scholar
He, Y. and Luo, J. (2017). “The novelty ‘sweet spot’ of invention”. Design Science, Vol. 3, p. e21.10.1017/dsj.2017.23Google Scholar
Herring, S.R., Poon, C.M., Balasi, G.A. and Bailey, B.P. (2011), “TweetSpiration: leveraging social media for design inspiration”. CHI'11 on Human Factors in Computing Systems, ACM, pp. 2311–2316.Google Scholar
Howe, J. (2008), Crowdsourcing: How the power of the crowd is driving the future of business, Random House.Google Scholar
Iyer, L.R., Doboli, S., Minai, A.A., Brown, V.R., Levine, D.S. and Paulus, P.B. (2009), “Neural dynamics of idea generation and the effects of priming”. Neural Networks, Vol. 22, pp. 674–686.Google Scholar
Kim, D., Cerigo, D.B., Jeong, H. and Youn, H. (2016), “Technological novelty profile and invention's future impact”. EPJ Data Science, Vol. 5, p. 8.10.1140/epjds/s13688-016-0069-1Google Scholar
Kristensson, P., Gustafsson, A. and Archer, T. (2004), “Harnessing the creative potential among users”. Journal of product innovation management, Vol. 21, pp. 4–14.10.1111/j.0737-6782.2004.00050.xGoogle Scholar
Lim, S.Y.C., Camburn, B.A., Moreno, D., Huang, Z. and Wood, K. (2016), “Design Concept Structures in Massive Group Ideation”. ASME International Design Engineering Technical Conferences and Computers and Information in Engineering Conference.10.1115/DETC2016-59805Google Scholar
Linsey, J., Markman, A. and Wood, K. (2012), “Design by analogy: a study of the WordTree method for problem re-representation”. Journal of Mechanical Design, Vol. 134, p. 041009.10.1115/1.4006145Google Scholar
Lohmann, S., Ziegler, J. and Tetzlaff, L. (2009), “Comparison of tag cloud layouts: Task-related performance and visual exploration”. IFIP Conference on Human-Computer Interaction, Springer, pp. 392–404.10.1007/978-3-642-03655-2_43Google Scholar
Pazienza, M.T., Pennacchiotti, M. and Zanzotto, F. M. (2005), “Terminology extraction: an analysis of linguistic and statistical approaches”. Knowledge mining. Springer.Google Scholar
Pedersen, J., Kocsis, D., Tripathi, A., Tarrell, A., Weerakoon, A., Tahmasbi, N., Xiong, J., Deng, W., Oh, O. and De Vreede, G.-J. (2013), “Conceptual foundations of crowdsourcing: A review of IS research”. System Sciences (HICSS), 46th Hawaii International Conference on, 2013. IEEE, pp. 579–588.Google Scholar
Poetz, M. K. and Schreier, M. (2012), “The value of crowdsourcing: can users really compete with professionals in generating new product ideas?” Journal of product innovation management, Vol. 29, pp. 245–256.10.1111/j.1540-5885.2011.00893.xGoogle Scholar
Schubert, E., Spitz, A., Weiler, M., Geiß, J. and Gertz, M. (2017), “Semantic Word Clouds with Background Corpus Normalization and t-distributed Stochastic Neighbor Embedding”. arXiv preprint arXiv:Vol. 1708. p. 03569.Google Scholar
Schuurman, D., Baccarne, B., De Marez, L. and Mechant, P. (2012), “Smart ideas for smart cities: investigating crowdsourcing for generating and selecting ideas for ICT innovation in a city context”. Journal of theoretical and applied electronic commerce research, Vol. 7, pp. 49–62.Google Scholar
Simonton, D.K. (1999), “Creativity as blind variation and selective retention: Is the creative process Darwinian?” Psychological Inquiry, Vol. 10, pp. 309–328.Google Scholar
Song, B., Luo, J., Mohan, R.E. and Wood, K.L. (2018), “Data-Driven Function Network Analysis for Product Platform Planning: A Case Study of Spherical Rolling Robots”. International Design Engineering Technical Conferences & Computers and Information in Engineering Conference (IDETC/CIE 2018).Google Scholar
Surowiecki, J. and Silverman, M.P. (2007), “The wisdom of crowds”. American Journal of Physics, Vol. 75, pp. 190–192.Google Scholar
Taramigkou, M., Paraskevopoulos, F., Bothos, E., Apostolou, D. and Mentzas, G. (2014), “Leveraging user inspiration with microblogging-driven exploratory search”. International Conference on Advanced Information Systems Engineering. Springer, pp. 238–249.Google Scholar
Taura, T. and Nagai, Y. (2012), “Concept generation for design creativity: a systematized theory and methodology”, Springer Science & Business Media.10.1007/978-1-4471-4081-8_4Google Scholar
Yilmaz, S., Seifert, C.M. and Gonzalez, R. (2010), “Cognitive heuristics in design: Instructional strategies to increase creativity in idea generation”. AI EDAM, Vol. 24, pp. 335–355.Google Scholar
Youn, H., Strumsky, D., Bettencourt, L. M. and Lobo, J. (2015), “Invention as a combinatorial process: evidence from US patents”. Journal of The Royal Society Interface, Vol. 12, p. 20150272.Google Scholar