Mapping the evolution of research topics using ATM and SNA
YE Chunlei
2014-12-25
发表期刊Chinese Journal of Library and Information Science
卷号7期号:4页码:46-62
摘要
Purpose: This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level, covering a research topic’s evolution trend, evolution path and its content changes over time.

Design/methodology/approach: After the topics were recovered by the author-topic model, we first built the keyword-topic co-occurrence network to track the dynamics of topic trends. Then a single-mode network was constructed with each node representing a topic and edge indicating the relationship between topics. It was used to illustrate the evolution path and content changes of research topics. A case study was conducted on the digital library research in China to verify the effectiveness of the analysis framework.

Findings: The experimental results show that this analysis framework can be used to track evolution of research topics at a micro level and using social network analysis method can help understand research topics’ evolution paths and content changes with the passage of time.

Research limitations: Using the analysis framework will produce limited results when examining unstructured data such as social media data. In addition, the effectiveness of the framework introduced in this paper needs to be verified with more research topics in information science and in more scientific fields.

Practical implications: This analysis framework can help scholars and researchers map research topics’ evolution process and gain insights into how a field’s topics have evolved over time.

Originality/value: The analysis framework used in this study can help reveal more micro evolution details. The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics, which helps better understand research topics’ evolution paths and content changes.
;
Purpose: This paper introduces an analysis framework for tracking the evolution of research topics at the selected topics level, covering a research topic’s evolution trend, evolution path and its content changes over time.

Design/methodology/approach: After the topics were recovered by the author-topic model, we first built the keyword-topic co-occurrence network to track the dynamics of topic trends. Then a single-mode network was constructed with each node representing a topic and edge indicating the relationship between topics. It was used to illustrate the evolution path and content changes of research topics. A case study was conducted on the digital library research in China to verify the effectiveness of the analysis framework.

Findings: The experimental results show that this analysis framework can be used to track evolution of research topics at a micro level and using social network analysis method can help understand research topics’ evolution paths and content changes with the passage of time.

Research limitations: Using the analysis framework will produce limited results when examining unstructured data such as social media data. In addition, the effectiveness of the framework introduced in this paper needs to be verified with more research topics in information science and in more scientific fields.

Practical implications: This analysis framework can help scholars and researchers map research topics’ evolution process and gain insights into how a field’s topics have evolved over time.

Originality/value: The analysis framework used in this study can help reveal more micro evolution details. The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics, which helps better understand research topics’ evolution paths and content changes.
文章类型Research Paper
关键词Topic Evolution Social Network Analysis (Sna) Author-topic Model (Atm) Digital Library Topic Network
学科领域新闻学与传播学 ; 图书馆、情报与文献学
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收录类别其他
语种英语
文献类型期刊论文
条目标识符http://ir.las.ac.cn/handle/12502/7625
专题Journal of Data and Information Science_Chinese Journal of Library and Information Science-2014
作者单位Beijing University of Agriculture Library, Beijing 102206, China
推荐引用方式
GB/T 7714
YE Chunlei. Mapping the evolution of research topics using ATM and SNA[J]. Chinese Journal of Library and Information Science,2014,7(4):46-62.
APA YE Chunlei.(2014).Mapping the evolution of research topics using ATM and SNA.Chinese Journal of Library and Information Science,7(4),46-62.
MLA YE Chunlei."Mapping the evolution of research topics using ATM and SNA".Chinese Journal of Library and Information Science 7.4(2014):46-62.
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