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| Analyzing the Time-lag Effect in Scientific Research Within the Same Field at Home and Abroad: Focusing on Data Mining |
| Tan Chunhui, Xiong Mengyuan |
| School of Information Management, Central China Normal University, Wuhan 430079 |
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Abstract There is a difference in the scientific research level seen between domestic and international academic systems of the same scientific field. By analyzing the time-lag effect in topics and number of publications of domestic and international core journal articles in the same field, horizontal comparison in the field can be performed and the level of scientific research development can be measured. To begin with, the method and procedure of measuring the topic-based and number-based time lag in articles from domestic and international core journals are proposed. Then, taking the field of data mining as an example, literature records of core journal articles included in the CNKI (China National Knowledge Infrastructure) and WoS (Web of Science) databases from 1996 to 2019 are collected. First, the LDA (latent Dirichlet allocation) model is used for topic extraction from the literature by time-slicing, and similarity is applied on the extracted topics to measure the topic-based time-lag effect, which helps to reveal the most significant lagging direction and period in the field of Data Mining. Second, the ARDL (auto-regressive distributed lag) model is used to model and analyze the time series, which is composed of the data on the number of annual publications of domestic and international journals. By using this model, the most significant lagging coefficient can be found to identify the corresponding number-based lagging direction and period. Results suggest that in the field of Data Mining, domestic research lags behind international research with a topic-based time-lag of 3 years, with 38.6% of domestic topics lagging behind. Domestic journals also lag behind international journals in terms of number of publications with a time lag of 5 years, but the lag coefficient turns out to be 1.431913. The results prove that the proposed measurement methods for time-lag effect in different academic systems are adaptable under most circumstances.
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Received: 16 March 2020
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