Dynamics of topic formation and quantitative analysis of hot trends in physical science
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Dynamics of topic formation and quantitative analysis of hot trends in physical science A. V. Chumachenko1 · B. G. Kreminskyi2 · Iu. L. Mosenkis3 · A. I. Yakimenko1,2,3 Received: 20 April 2020 © Akadémiai Kiadó, Budapest, Hungary 2020
Abstract Successful research in the face of increasing complexity of modern scientific knowledge together with diversity and depth of the studied problems requires an understanding of the structure and evolution of trends in science. Available digital records open wide possibilities for statistical analysis of scientific publications and related metadata for topic modeling and evolution, knowledge mapping, citation indexing, etc. We investigate dynamical properties of the physical topics using analysis of temporal evolution of proximity measure for word pairs related to the mutual information. We use full-text conceptualization of content of scientific documents provided by the ScienceWISE platform for topic mapping, trend analysis and detection of hot topics together with relevant papers retrieval. We found that time evolution of relative mutual information distance reveals a hidden topic structure and could be used for quantitative analysis of current trends in scientific research. Keywords Dynamical complex networks · Topic detection · Mutual information dynamics · Topic structure · Concept mapping Mathematics Subject Classification 94A17 · 05C62 · 37E25
Introduction Progress and effective scientific research grounded on structured preliminary knowledge and information exchange. The vastly growing amount of information from researchers in all branches of science that we can see in our days requires efficient mapping and understanding. To solve this problem various methods of document clustering (Aggarwal and Zhai 2012; Cai et al. 2011; Lu et al. 2011; Ng et al. 2002; Xu and Gong 2004; Xu et al. * A. V. Chumachenko [email protected] 1
Department of Physics, Taras Shevchenko National University of Kyiv, 64/13, Volodymyrska Street, Kiev 01601, Ukraine
2
State Scientific Institution “Institute of Education Content Modernization”, 36, Mytropolyta Vasylia Lypkivskogo Str., Kiev 03035, Ukraine
3
Institute of Philology, Taras Shevchenko National University of Kyiv, 64/13, Volodymyrska Street, Kiev 01601, Ukraine
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Vol.:(0123456789)
Scientometrics
2003), topic modeling (Blei et al. 2003; Hofmann 2001) and a mixture of both (Xie and Xing 2013) where developed. While document clustering combines similar scientific documents into collections, which is important for the organization, browsing, summarization, classification and retrieval of relevant documents, the thematic modeling develops predictive models for discovering semantic structures (e.g. topics) embedded in a collection of documents (Xie and Xing 2013). A particular interest of the scientific community is attracted to the problem of hot topic detection (Wang and Fang 2016; Wen and Huang 2012; Dong et al. 2012; Chen et al. 2015; Tan et al. 2014). For researchers, hot topics are the most attractive research qu
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