Measuring the evolving stage of temporal distribution of research topic keyword in scientific literature by research heat curve

Junsheng Zhang (), Xiaoping Sun () and Zhihui Liu ()
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Junsheng Zhang: Institute of Scientific and Technical Information of China
Xiaoping Sun: Chinese Academy of Sciences
Zhihui Liu: Institute of Scientific and Technical Information of China

Scientometrics, 2024, vol. 129, issue 11, No 35, 7287-7328

Abstract: Abstract Scientific literature records research progresses of science and technology. Research topics of technologies are evolving in scientific literature. The temporal distribution of the research topic keywords in literature can reflect the evolving stages of a research topic over time. A research topic can be in different evolving stages with different evolving distributions. Previous work mainly focused on visualizing the temporal distribution of keyword weights to illustrate the developing history and trend of a research topic in a literature collection. Quantitatively measuring the evolving stage of a research topic keyword by a baseline distribution can help to detect topic evolving stages in a large scientific literature corpus in an automatic way. How to build a quantitative baseline and how to quantitative compare the topic temporal distribution with the baseline distribution are two challenges. In this paper, an explicit function of the research heat curve is obtained by constructing a differential equation system of evolving research population groups within a research community on a research topic represented by a topic keyword. Six segments of the heat curve are obtained by zero points of derivatives of the heat curve, which together with the full heat curve are used as the quantitative baselines for measuring the temporal distribution of a research topic in different evolving stages. The temporal distribution of a research topic keyword in a scientific literature collection is obtained from the TF-IDF features of the literature collection. A curve shape matching algorithm is designed to match the temporal distribution curve with each baseline segment of the heat curve function to obtain a distance by measuring the shape similarity between the baseline segment curve and the temporal distribution curve. The segment with the smallest distance is used as a quantitative indicator of the evolving stage of the research topic. Experiments on the produced distributions and the real distributions confirm the effectiveness of the heat curve matching method for measuring the evolving stages from the temporal distribution of topics.

Keywords: Research heat curve function; Evolving stage; Keyword temporal distribution; Curve shape matching; Differential equation (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s11192-024-04937-0

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