Due to the fast development of Internet, the bibliographic information under “Internet+” era presents obvious big data characteristics. Crowdsourcing strategy can be applied to analyze bibliographic data. By gathering journal papers and conference papers from different sources in a specific field and identifying its core authors as well as the evolution of the hot research topics the core authors chose to work on, we hope to provide a reference for future research topics and research directions. In this paper, we modified an existing core author evaluation system by adding an indicator called the weighted score of every authorship. After gathering the experimental data from International Education of Chinese, we used the Price formula to detect the core author candidates and build a core author evaluation system based on five indicators, which were the publication volumes, the number of citations, h-index, research field normalized citation impact, and weighted score of every authorship. The weight of each indicator and the score of each candidate were calculated based on the comprehensive index and entropy-based method. Thereafter, the Latent Dirichlet Allocation (LDA) model was used to summarize the papers on the hot research topics the core authors worked on for each period, followed by evolution analysis. Using the above methods, experiments were carried out on the bibliographic data of journal papers and conference papers in the discipline of International Education of Chinese between 2001 and 2020 that were included in the CNKI. Scholars whose scores were greater than 50 were identified as the core author(s) of each period. The core authors and the evolution of their hot research topics were conducted in four periods, namely 2001–2005, 2006–2010, 2011–2015, and 2016–2020. It is our conclusion that the compilation of textbooks has always been the focus of the discipline of International Education of Chinese, while the teaching modes have become increasingly diversified, and also research on the training of undergraduates and postgraduates has increased. With the advancement of technology, much relevant knowledge from many other disciplines has been integrated into the International Education of Chinese, and scholars should pay attention to interdisciplinary research.
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Open Access
Research paper
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Identifying the frontiers of a specific research field is one of the most basic tasks in bibliometrics and research published in leading conferences is crucial to the data mining research community, whereas few research studies have focused on it. The purpose of this study is to detect the intellectual structure of data mining based on conference papers.
This study takes the authoritative conference papers of the ranking 9 in the data mining field provided by Google Scholar Metrics as a sample. According to paper amount, this paper first detects the annual situation of the published documents and the distribution of the published conferences. Furthermore, from the research perspective of keywords, CiteSpace was used to dig into the conference papers to identify the frontiers of data mining, which focus on keywords term frequency, keywords betweenness centrality, keywords clustering and burst keywords.
Research showed that the research heat of data mining had experienced a linear upward trend during 2007 and 2016. The frontier identification based on the conference papers showed that there were five research hotspots in data mining, including clustering, classification, recommendation, social network analysis and community detection. The research contents embodied in the conference papers were also very rich.
This study detected the research frontier from leading data mining conference papers. Based on the keyword co-occurrence network, from four dimensions of keyword term frequency, betweeness centrality, clustering analysis and burst analysis, this paper identified and analyzed the research frontiers of data mining discipline from 2007 to 2016.
