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Tracking the dynamics of co-word networks for emerging topic identification

Author

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  • Huang, Lu
  • Chen, Xiang
  • Ni, Xingxing
  • Liu, Jiarun
  • Cao, Xiaoli
  • Wang, Changtian

Abstract

Identifying emerging topics has been an essential study for nations to develop strategic priorities, for enterprises to create business strategies, and for institutions to define research areas. However, how to characterize emerging topics effectively and comprehensively is still very challenging. This study proposes a framework for identifying emerging topics based on a dynamic co-word network analysis, which integrates a link prediction model with machine learning techniques. Time-sliced co-word networks are weighted according to the frequency of terms' co-occurrence. A back-propagation neural network is used to forecast a future network by predicting linkages among unconnected nodes based on existing links. Four indicators are then used to sort out potential candidates of emerging topics in the predicted network. A case study on information science demonstrates the reliability of the proposed methodology, followed by subsequent empirical and expert validations.

Suggested Citation

  • Huang, Lu & Chen, Xiang & Ni, Xingxing & Liu, Jiarun & Cao, Xiaoli & Wang, Changtian, 2021. "Tracking the dynamics of co-word networks for emerging topic identification," Technological Forecasting and Social Change, Elsevier, vol. 170(C).
    • Handle: RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521003760
    DOI: 10.1016/j.techfore.2021.120944
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    Cited by:

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    2. Guiqiong Xu & Chen Dong & Lei Meng, 2022. "Research on the Collaborative Innovation Relationship of Artificial Intelligence Technology in Yangtze River Delta of China: A Complex Network Perspective," Sustainability, MDPI, vol. 14(21), pages 1-19, October.
    3. Liu, Zhenfeng & Feng, Jian & Uden, Lorna, 2023. "Technology opportunity analysis using hierarchical semantic networks and dual link prediction," Technovation, Elsevier, vol. 128(C).
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    5. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    6. Naeini, Ali Bonyadi & Zamani, Mehdi & Daim, Tugrul U. & Sharma, Mahak & Yalcin, Haydar, 2022. "Conceptual structure and perspectives on “innovation management”: A bibliometric review," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    7. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    8. Ryosuke L. Ohniwa & Kunio Takeyasu & Aiko Hibino, 2022. "Researcher dynamics in the generation of emerging topics in life sciences and medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 871-884, February.

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