Mapping the evolution of research topics using ATM and SNA
YE Chunlei
2014-12-25
Source PublicationChinese Journal of Library and Information Science
Volume7Issue:4Pages:46-62
Abstract
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.
SubtypeResearch Paper
KeywordTopic Evolution Social Network Analysis (Sna) Author-topic Model (Atm) Digital Library Topic Network
Subject Area新闻学与传播学 ; 图书馆、情报与文献学
URL查看原文
Indexed By其他
Language英语
Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/7625
CollectionJournal of Data and Information Science_Chinese Journal of Library and Information Science-2014
AffiliationBeijing University of Agriculture Library, Beijing 102206, China
Recommended Citation
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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