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Identifying potential breakthrough research: A machine learning method using scientific papers and Twitter data

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  • Li, Xin
  • Wen, Yang
  • Jiang, Jiaojiao
  • Daim, Tugrul
  • Huang, Lucheng

Abstract

Breakthrough research may signal shifts in science, technology, and innovation systems. Early identification of breakthrough research is important not only for scientists, but also for policy makers and R&D experts in developing R&D strategies and allocating R&D resources. Researchers mostly use scientific papers data to identify potential breakthrough research, but they rarely make use of Twitter data related to scientific research and machine learning methods. Analysis of Twitter data is of great significance for us to understand the public's perception of potential breakthrough research and to identify potential breakthrough research. Machine learning methods can assist us in predicting the trend of events by utilizing prior knowledge and experience. Therefore, this paper proposes a framework for identifying potential breakthrough research using machine learning methods with scientific papers and Twitter data. We select solar cells as a case study to verify the valid and flexible of this framework. In this case, we use machine learning method to discover potential breakthrough research from scientific papers, and we use Twitter data mining to analyze Twitter users' sense of and response to the discovered potential breakthrough research, which aims to achieve a more extensive and diverse assessment of the discovered potential breakthrough research. This paper contributes to identifying potential breakthrough research, as well as understanding the emergence and development of breakthrough research. It will be of interest to R&D experts in the field of solar cell technology.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:tefoso:v:184:y:2022:i:c:s0040162522005637
    DOI: 10.1016/j.techfore.2022.122042
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    1. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    2. 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.
    3. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    4. Hendrik P. van Dalen & Kène Henkens, 1999. "How Influential Are Demography Journals?," Population and Development Review, The Population Council, Inc., vol. 25(2), pages 229-251, June.
    5. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    6. Nick Haslam & Lauren Ban & Leah Kaufmann & Stephen Loughnan & Kim Peters & Jennifer Whelan & Sam Wilson, 2008. "What makes an article influential? Predicting impact in social and personality psychology," Scientometrics, Springer;Akadémiai Kiadó, vol. 76(1), pages 169-185, July.
    7. Ronald N. Kostoff, 2007. "The difference between highly and poorly cited medical articles in the journal Lancet," Scientometrics, Springer;Akadémiai Kiadó, vol. 72(3), pages 513-520, September.
    8. Laudel, Grit & Gläser, Jochen, 2014. "Beyond breakthrough research: Epistemic properties of research and their consequences for research funding," Research Policy, Elsevier, vol. 43(7), pages 1204-1216.
    9. Wanying Ding & Chaomei Chen, 2014. "Dynamic topic detection and tracking: A comparison of HDP, C-word, and cocitation methods," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(10), pages 2084-2097, October.
    10. Robert J. W. Tijssen & Martijn S. Visser & Thed N. van Leeuwen, 2002. "Benchmarking international scientific excellence: Are highly cited research papers an appropriate frame of reference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(3), pages 381-397, July.
    11. Min, Chao & Bu, Yi & Sun, Jianjun, 2021. "Predicting scientific breakthroughs based on knowledge structure variations," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    12. Jesper W. Schneider & Rodrigo Costas, 2017. "Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 709-723, March.
    13. Rickard Danell, 2011. "Can the quality of scientific work be predicted using information on the author's track record?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(1), pages 50-60, January.
    14. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    15. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    16. Marc Julius & Charles E. Berkoff & Alvin E. Strack & Frank Krasovec & A. Douglas Bender, 1977. "A very early warning system for the rapid identification and transfer of new technology," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 28(3), pages 170-174, May.
    17. Holly N. Wolcott & Matthew J. Fouch & Elizabeth R. Hsu & Leo G. DiJoseph & Catherine A. Bernaciak & James G. Corrigan & Duane E. Williams, 2016. "Modeling time-dependent and -independent indicators to facilitate identification of breakthrough research papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 807-817, May.
    18. Alan L. Porter & Alisa Kongthon & Jye-Chyi (JC) Lu, 2002. "Research profiling: Improving the literature review," Scientometrics, Springer;Akadémiai Kiadó, vol. 53(3), pages 351-370, March.
    19. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    20. Hendrik P. Van Dalen & Kène Henkens, 2001. "What makes a scientific article influential? The case of demographers," Scientometrics, Springer;Akadémiai Kiadó, vol. 50(3), pages 455-482, March.
    21. Chai, Sen & Menon, Anoop, 2019. "Breakthrough recognition: Bias against novelty and competition for attention," Research Policy, Elsevier, vol. 48(3), pages 733-747.
    22. Ponomarev, Ilya V. & Williams, Duane E. & Hackett, Charles J. & Schnell, Joshua D. & Haak, Laurel L., 2014. "Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 49-55.
    23. Tian Yu & Guang Yu & Peng-Yu Li & Liang Wang, 2014. "Citation impact prediction for scientific papers using stepwise regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1233-1252, November.
    24. Nofer, Michael & Hinz, Oliver, 2015. "Using Twitter to Predict the Stock Market: Where is the Mood Effect?," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 77140, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    25. J. J. Winnink & Robert J. W. Tijssen, 2015. "Early stage identification of breakthroughs at the interface of science and technology: lessons drawn from a landmark publication," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 113-134, January.
    26. Porter, Alan L. & Chiavetta, Denise & Newman, Nils C., 2020. "Measuring tech emergence: A contest," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    27. Ilya V. Ponomarev & Brian K. Lawton & Duane E. Williams & Joshua D. Schnell, 2014. "Breakthrough paper indicator 2.0: can geographical diversity and interdisciplinarity improve the accuracy of outstanding papers prediction?," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(3), pages 755-765, September.
    28. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
    29. Dag W Aksnes, 2003. "Characteristics of highly cited papers," Research Evaluation, Oxford University Press, vol. 12(3), pages 159-170, December.
    30. Michael Nofer & Oliver Hinz, 2015. "Using Twitter to Predict the Stock Market," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 57(4), pages 229-242, August.
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    6. Chiarello, Filippo & Giordano, Vito & Spada, Irene & Barandoni, Simone & Fantoni, Gualtiero, 2024. "Future applications of generative large language models: A data-driven case study on ChatGPT," Technovation, Elsevier, vol. 133(C).

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