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Detecting bursty terms in computer science research

Author

Listed:
  • E. Tattershall

    (The University of Manchester)

  • G. Nenadic

    (The University of Manchester)

  • R. D. Stevens

    (The University of Manchester)

Abstract

Research topics rise and fall in popularity over time, some more swiftly than others. The fastest rising topics are typically called bursts; for example “deep learning”, “internet of things” and “big data”. Being able to automatically detect and track bursty terms in the literature could give insight into how scientific thought evolves over time. In this paper, we take a trend detection algorithm from stock market analysis and apply it to over 30 years of computer science research abstracts, treating the prevalence of each term in the dataset like the price of a stock. Unlike previous work in this domain, we use the free text of abstracts and titles, resulting in a finer-grained analysis. We report a list of bursty terms, and then use historical data to build a classifier to predict whether they will rise or fall in popularity in the future, obtaining accuracy in the region of 80%. The proposed methodology can be applied to any time-ordered collection of text to yield past and present bursty terms and predict their probable fate.

Suggested Citation

  • E. Tattershall & G. Nenadic & R. D. Stevens, 2020. "Detecting bursty terms in computer science research," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 681-699, January.
    • Handle: RePEc:spr:scient:v:122:y:2020:i:1:d:10.1007_s11192-019-03307-5
    DOI: 10.1007/s11192-019-03307-5
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    References listed on IDEAS

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    1. Antonio Cavacini, 2015. "What is the best database for computer science journal articles?," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2059-2071, March.
    2. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
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    Cited by:

    1. E. Tattershall & G. Nenadic & R. D. Stevens, 2021. "Modelling trend life cycles in scientific research using the Logistic and Gompertz equations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9113-9132, November.
    2. Bastian Schaefermeier & Gerd Stumme & Tom Hanika, 2021. "Topic space trajectories," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5759-5795, July.
    3. Guilherme Belloque & Martina K Linnenluecke & Mauricio Marrone & Abhay K Singh & Rui Xue, 2021. "55 years of Abacus: Evolution of Research Streams and Future Research Directions," Abacus, Accounting Foundation, University of Sydney, vol. 57(3), pages 593-618, September.
    4. Martina K. Linnenluecke & Mauricio Marrone & Abhay K. Singh, 2020. "Sixty years of Accounting & Finance: a bibliometric analysis of major research themes and contributions," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(4), pages 3217-3251, December.

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