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Searching for new breakthroughs in science: How effective are computerised detection algorithms?

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  • Winnink, J.J.
  • Tijssen, Robert J.W.
  • van Raan, A.F.J.

Abstract

In this study, we design, develop, implement and test an analytical framework and measurement model to detect scientific discoveries with ‘breakthrough’ characteristics. To do so, we have developed a series of computerised search algorithms that data mine large quantities of research publications. These algorithms facilitate early-stage detection of ‘breakout’ papers that emerge as highly cited and distinctive and are considered to be potential breakthroughs. Combining computer-aided data mining with decision heuristics, enabled us to assess structural changes within citation patterns with the international scientific literature. In our case studies, we applied a citation impact time window of 24–36 months after publication of each research paper.

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  • Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    • Handle: RePEc:eee:tefoso:v:146:y:2019:i:c:p:673-686
    DOI: 10.1016/j.techfore.2018.05.018
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    6. Li, Xin & Wen, Yang & Jiang, Jiaojiao & Daim, Tugrul & Huang, Lucheng, 2022. "Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    7. Heng Huang & Donghua Zhu & Xuefeng Wang, 2022. "Evaluating scientific impact of publications: combining citation polarity and purpose," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5257-5281, September.

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