Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2983323.2983820acmconferencesArticle/Chapter ViewAbstractPublication PagescikmConference Proceedingsconference-collections
research-article
Public Access

Inspiration or Preparation?: Explaining Creativity in Scientific Enterprise

Published: 24 October 2016 Publication History

Abstract

Human creativity is the ultimate driving force behind scientific progress. While the building blocks of innovations are often embodied in existing knowledge, it is creativity that blends seemingly disparate ideas. Existing studies have made striding advances in quantifying creativity of scientific publications by investigating their citation relationships. Yet, little is known hitherto about the underlying mechanisms governing scientific creative processes, largely due to that a paper's references, at best, only partially reflect its authors' actual information consumption. This work represents an initial step towards fine-grained understanding of creative processes in scientific enterprise. In specific, using two web-scale longitudinal datasets (120.1 million papers and 53.5 billion web requests spanning 4 years), we directly contrast authors' information consumption behaviors against their knowledge products. We find that, of 59.0% papers across all scientific fields, 25.7% of their creativity can be readily explained by information consumed by their authors. Further, by leveraging these findings, we develop a predictive framework that accurately identifies the most critical knowledge to fostering target scientific innovations. We believe that our framework is of fundamental importance to the study of scientific creativity. It promotes strategies to stimulate and potentially automate creative processes, and provides insights towards more effective designs of information recommendation platforms.

References

[1]
R. Agrawal and R. Srikant. Fast algorithms for mining association rules in large databases. In Proceedings of the 20th International Conference on Very Large Data Bases, VLDB '94, 1994.
[2]
S. Boyd and L. Vandenberghe. Convex Optimization. Cambridge University Press, New York, NY, USA, 2004.
[3]
J. Cheng, L. Adamic, P. A. Dow, J. M. Kleinberg, and J. Leskovec. Can cascades be predicted? In Proceedings of the 23rd International Conference on World Wide Web, WWW '14, 2014.
[4]
R. Collins. The Sociology of Philosophies: A Global Theory of Intellectual Change. Belknap Press of Harvard University Press, 1998.
[5]
S. Colton. Creativity versus the perception of creativity in computational systems. In AAAI Spring Symposium: Creative Intelligent Systems'08, 2008.
[6]
M. Csikszentmihalyi. Creativity-flow and the psychology of discovery and invention. Harper perennial, 1996.
[7]
T. De Smedt. Modeling Creativity: Case Studies in Python. ArXiv e-prints, 2014.
[8]
S. Doboli, F. Zhao, and A. Doboli. New measures for evaluating creativity in scientific publications. ArXiv e-prints, 2014.
[9]
Y. Dong, R. A. Johnson, and N. V. Chawla. Will this paper increase your h-index?: Scientific impact prediction. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining, WSDM '15, 2015.
[10]
S. N. Dorogovtsev and J. F. F. Mendes. Evolution of Networks: From Biological Nets to the Internet and WWW (Physics). Oxford University Press, Inc., New York, NY, USA, 2003.
[11]
J. A. Evans and J. G. Foster. Metaknowledge. Science, 331(6018):721--725, 2011.
[12]
L. Fleming. Recombinant uncertainty in technological search. Management Science, 47(1):117--132, 2001.
[13]
L. Gabora and A. Saab. Creative interference and states of potentiality in analogy problem solving. In Proceedings of the Annual Meeting of the Cognitive Science Society, COGSCI '13, 2013.
[14]
M. R. Guevara, D. Hartmann, M. Aristarán, M. Mendoza, and C. A. Hidalgo. The Research Space: using the career paths of scholars to predict the evolution of the research output of individuals, institutions, and nations. ArXiv e-prints, 2016.
[15]
B. F. Jones, S. Wuchty, and B. Uzzi. Multi-university research teams: Shifting impact, geography, and stratification in science. Science, 322(5905):1259--1262, 2008.
[16]
D. Kim, D. Burkhardt Cerigo, H. Jeong, and H. Youn. Technological novelty profile and invention's future impact. ArXiv e-prints, 2015.
[17]
A. Koestler. The Act of Creation. Arkana, 1964.
[18]
L. Li and H. Tong. The child is father of the man: Foresee the success at the early stage. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '15, 2015.
[19]
M. Meiss, F. Menczer, S. Fortunato, A. Flammini, and A. Vespignani. Ranking web sites with real user traffic. In Proc. First ACM International Conference on Web Search and Data Mining (WSDM), 2008.
[20]
G. L. Nemhauser, L. A. Wolsey, and M. L. Fisher. An analysis of approximations for maximizing submodular set functions--i. Mathematical Programming, 14(1):265--294, 1978.
[21]
Plato. The republic, Book X.
[22]
M. Rosvall and C. T. Bergstrom. Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences, 105(4):1118--1123, 2008.
[23]
R. Saunders and J. S. Gero. Artificial creativity: A synthetic approach to the study of creative behaviour. In Computational and Cognitive Models of Creative Design V, 2001.
[24]
W. Shadish and S. Fuller. The social psychology of science. Guilford Press, 1994.
[25]
A. Sinha, Z. Shen, Y. Song, H. Ma, D. Eide, B.-J. P. Hsu, and K. Wang. An overview of microsoft academic service (mas) and applications. In Proceedings of the 24th International Conference on World Wide Web, WWW '15 Companion, 2015.
[26]
B. Uzzi, S. Mukherjee, M. Stringer, and B. Jones. Atypical combinations and scientific impact. Science, 342(6157):468--472, 2013.
[27]
T. Veale and Y. Hao. Learning to understand figurative language: From similes to metaphors to irony. In Proceedings of the Annual Meeting of the Cognitive Science Society, COGSCI '07, 2007.
[28]
D. Wang, C. Song, and A.-L. Barabási. Quantifying long-term scientific impact. Science, 342(6154):127--132, 2013.
[29]
M. Weitzman. Recombinant growth. Quarterly Journal of Economics, 113(2):331--360, 1998.
[30]
G. A. Wiggins. A preliminary framework for description, analysis and comparison of creative systems. Know.-Based Syst., 19(7):449--458, 2006.

Cited By

View all
  • (2020)A model of the transition to behavioural and cognitive modernity using reflexively autocatalytic networksJournal of The Royal Society Interface10.1098/rsif.2020.054517:171(20200545)Online publication date: 28-Oct-2020

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
October 2016
2566 pages
ISBN:9781450340731
DOI:10.1145/2983323
Permission to make digital or hard copies of all or part 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 components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 October 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. creative process
  2. knowledge production
  3. science of science

Qualifiers

  • Research-article

Funding Sources

Conference

CIKM'16
Sponsor:
CIKM'16: ACM Conference on Information and Knowledge Management
October 24 - 28, 2016
Indiana, Indianapolis, USA

Acceptance Rates

CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;
Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)57
  • Downloads (Last 6 weeks)6
Reflects downloads up to 30 Aug 2024

Other Metrics

Citations

Cited By

View all
  • (2020)A model of the transition to behavioural and cognitive modernity using reflexively autocatalytic networksJournal of The Royal Society Interface10.1098/rsif.2020.054517:171(20200545)Online publication date: 28-Oct-2020

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Get Access

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media