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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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    5. Javier Jiménez-Cabas & Lizeth Torres & Jorge de J. Lozoya-Santos, 2023. "Twitter Data Mining for the Diagnosis of Leaks in Drinking Water Distribution Networks," Sustainability, MDPI, vol. 15(6), pages 1-16, March.
    6. Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2024. "The impact of ChatGPT on human skills: A quantitative study on twitter data," Technological Forecasting and Social Change, Elsevier, vol. 203(C).

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