IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v122y2020i1d10.1007_s11192-019-03307-5.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-019-03307-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-019-03307-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. Antonio Cavacini, 2015. "What is the best database for computer science journal articles?," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 2059-2071, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramona Weinrich, 2019. "Opportunities for the Adoption of Health-Based Sustainable Dietary Patterns: A Review on Consumer Research of Meat Substitutes," Sustainability, MDPI, vol. 11(15), pages 1-15, July.
    2. Piers Steel & Sjoerd Beugelsdijk & Herman Aguinis, 2021. "The anatomy of an award-winning meta-analysis: Recommendations for authors, reviewers, and readers of meta-analytic reviews," Journal of International Business Studies, Palgrave Macmillan;Academy of International Business, vol. 52(1), pages 23-44, February.
    3. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.
    4. Augusteijn, Hilde Elisabeth Maria & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2021. "Posterior Probabilities of Effect Sizes and Heterogeneity in Meta-Analysis: An Intuitive Approach of Dealing with Publication Bias," OSF Preprints avkgj, Center for Open Science.
    5. Ruhua Huang & Yuting Huang & Fan Qi & Leyi Shi & Baiyang Li & Wei Yu, 2022. "Exploring the characteristics of special issues: distribution, topicality, and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5233-5256, September.
    6. Neal R. Haddaway & Max W. Callaghan & Alexandra M. Collins & William F. Lamb & Jan C. Minx & James Thomas & Denny John, 2020. "On the use of computer‐assistance to facilitate systematic mapping," Campbell Systematic Reviews, John Wiley & Sons, vol. 16(4), December.
    7. Vincent Raoult, 2020. "How Many Papers Should Scientists Be Reviewing? An Analysis Using Verified Peer Review Reports," Publications, MDPI, vol. 8(1), pages 1-9, January.
    8. Eloy López-Meneses & Esteban Vázquez-Cano & Mariana-Daniela González-Zamar & Emilio Abad-Segura, 2020. "Socioeconomic Effects in Cyberbullying: Global Research Trends in the Educational Context," IJERPH, MDPI, vol. 17(12), pages 1-31, June.
    9. Jinseok Kim & Jinmo Kim & Jason Owen-Smith, 2019. "Generating automatically labeled data for author name disambiguation: an iterative clustering method," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 253-280, January.
    10. Soo Jeung Lee & Christian Schneijderberg & Yangson Kim & Isabel Steinhardt, 2021. "Have Academics’ Citation Patterns Changed in Response to the Rise of World University Rankings? A Test Using First-Citation Speeds," Sustainability, MDPI, vol. 13(17), pages 1-19, August.
    11. Lutz Bornmann & Robin Haunschild & Rüdiger Mutz, 2021. "Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-15, December.
    12. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2022. "Analysing academic paper ranking algorithms using test data and benchmarks: an investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4045-4074, July.
    13. Edré Moreira & Wagner Meira & Marcos André Gonçalves & Alberto H. F. Laender, 2023. "The rise of hyperprolific authors in computer science: characterization and implications," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2945-2974, May.
    14. Lanu Kim & Jason H. Portenoy & Jevin D. West & Katherine W. Stovel, 2020. "Scientific journals still matter in the era of academic search engines and preprint archives," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 71(10), pages 1218-1226, October.
    15. Ana Teresa Santos & Sandro Mendonça, 2022. "Do papers (really) match journals’ “aims and scope”? A computational assessment of innovation studies," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7449-7470, December.
    16. Jialiang Lin & Yao Yu & Yu Zhou & Zhiyang Zhou & Xiaodong Shi, 2020. "How many preprints have actually been printed and why: a case study of computer science preprints on arXiv," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 555-574, July.
    17. Jensen, Scott & Liu, Xiaozhong & Yu, Yingying & Milojevic, Staša, 2016. "Generation of topic evolution trees from heterogeneous bibliographic networks," Journal of Informetrics, Elsevier, vol. 10(2), pages 606-621.
    18. Bornmann, Lutz & Haunschild, Robin, 2022. "Empirical analysis of recent temporal dynamics of research fields: Annual publications in chemistry and related areas as an example," Journal of Informetrics, Elsevier, vol. 16(2).
    19. Carlo Galli & Stefano Guizzardi, 2021. "The Effect of Article Characteristics on Citation Number in a Diachronic Dataset of the Biomedical Literature on Chronic Inflammation: An Analysis by Ensemble Machines," Publications, MDPI, vol. 9(2), pages 1-11, April.
    20. Andrea Polonioli, 2020. "In search of better science: on the epistemic costs of systematic reviews and the need for a pluralistic stance to literature search," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1267-1274, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:122:y:2020:i:1:d:10.1007_s11192-019-03307-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.