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A comparison of methods for detecting hot topics

Author

Listed:
  • Yuen-Hsien Tseng

    (National Taiwan Normal University)

  • Yu-I Lin

    (Taipei Municipal University of Education)

  • Yi-Yang Lee

    (Taiwan Institute of Economic Research)

  • Wen-Chi Hung

    (National Applied Research Laboratories)

  • Chun-Hsiang Lee

    (National Applied Research Laboratories)

Abstract

In scientometrics for trend analysis, parameter choices for observing trends are often made ad hoc in past studies. For examples, different year spans might be used to create the time sequence and different indices were chosen for trend observation. However, the effectiveness of these choices was hardly known, quantitatively and comparatively. This work provides clues to better interpret the results when a certain choice was made. Specifically, by sorting research topics in decreasing order of interest predicted by a trend index and then by evaluating this ordering based on information retrieval measures, we compare a number of trend indices (percentage of increase vs. regression slope), trend formulations (simple trend vs. eigen-trend), and options (various year spans and durations for prediction) in different domains (safety agriculture and information retrieval) with different collection scales (72500 papers vs. 853 papers) to know which one leads to better trend observation. Our results show that the slope of linear regression on the time series performs constantly better than the others. More interestingly, this index is robust under different conditions and is hardly affected even when the collection was split into arbitrary (e.g., only two) periods. Implications of these results are discussed. Our work does not only provide a method to evaluate trend prediction performance for scientometrics, but also provides insights and reflections for past and future trend observation studies.

Suggested Citation

  • Yuen-Hsien Tseng & Yu-I Lin & Yi-Yang Lee & Wen-Chi Hung & Chun-Hsiang Lee, 2009. "A comparison of methods for detecting hot topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(1), pages 73-90, October.
    • Handle: RePEc:spr:scient:v:81:y:2009:i:1:d:10.1007_s11192-009-1885-x
    DOI: 10.1007/s11192-009-1885-x
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    References listed on IDEAS

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    1. O. Mryglod & Yu. Holovatch & R. Kenna & B. Berche, 2016. "Quantifying the evolution of a scientific topic: reaction of the academic community to the Chornobyl disaster," Scientometrics, Springer;Akadémiai Kiadó, vol. 106(3), pages 1151-1166, March.
    2. Yuen-Hsien Tseng & Ming-Yueh Tsay, 2013. "Journal clustering of library and information science for subfield delineation using the bibliometric analysis toolkit: CATAR," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(2), pages 503-528, May.
    3. Leonidas Akritidis & Dimitrios Katsaros & Panayiotis Bozanis, 2012. "Identifying attractive research fields for new scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(3), pages 869-894, June.
    4. Soroush Taheri & Sadegh Aliakbary, 2022. "Research trend prediction in computer science publications: a deep neural network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 849-869, February.
    5. Heeyong Noh & Sungjoo Lee, 2019. "Where technology transfer research originated and where it is going: a quantitative analysis of literature published between 1980 and 2015," The Journal of Technology Transfer, Springer, vol. 44(3), pages 700-740, June.
    6. Srđan Bojović & Rada Matić & Zorica Popović & Miroslava Smiljanić & Milena Stefanović & Vera Vidaković, 2014. "An overview of forestry journals in the period 2006–2010 as basis for ascertaining research trends," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(2), pages 1331-1346, February.
    7. Zhichao Fang & Rodrigo Costas & Wencan Tian & Xianwen Wang & Paul Wouters, 2020. "An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2519-2549, September.
    8. Ma, Jing & Abrams, Natalie F. & Porter, Alan L. & Zhu, Donghua & Farrell, Dorothy, 2019. "Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 767-775.
    9. Woondong Yeo & Seonho Kim & Byoung-Youl Coh & Jaewoo Kang, 2013. "A quantitative approach to recommend promising technologies for SME innovation: a case study on knowledge arbitrage from LCD to solar cell," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 589-604, August.
    10. Ryosuke L. Ohniwa & Aiko Hibino & Kunio Takeyasu, 2010. "Trends in research foci in life science fields over the last 30 years monitored by emerging topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 111-127, October.
    11. Antonio Fernández-Cano & Manuel Torralbo & Mónica Vallejo, 2012. "Time series of scientific growth in Spanish doctoral theses (1848–2009)," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 15-36, April.
    12. Yuen-Hsien Tseng & Chun-Yen Chang & M. Shane Tutwiler & Ming-Chao Lin & James P. Barufaldi, 2013. "A scientometric analysis of the effectiveness of Taiwan’s educational research projects," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 1141-1166, June.
    13. Jinlou Zhao & Hongyu Gao & Yongli Li & Jiaguo Liu, 2017. "Which factors affect the duration of hot topics on social media platforms?," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(5), pages 2395-2407, September.
    14. Chen, Kaihua & Guan, Jiancheng, 2011. "A bibliometric investigation of research performance in emerging nanobiopharmaceuticals," Journal of Informetrics, Elsevier, vol. 5(2), pages 233-247.

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