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

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

Technological Forecasting and Social Change, 2021, vol. 170, issue C

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.

Keywords: Emerging topics; Dynamic networks; Link prediction; Machine learning (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:170:y:2021:i:c:s0040162521003760

DOI: 10.1016/j.techfore.2021.120944

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