Mapping the topic evolution using citation-topic model and social network analysis

Abstract

This paper introduces an analysis framework to track the evolution of research topics based on the content including topic evolution trend, main evolution path and content changes over time. After the topics were identified by the citation-topic model, we first built a 2-mode co-occurrence (the keywords and topic co-occurrence) network to track the dynamics of topic trends. Then we built a single-mode network to illustrate the evolution path and content changes of research topics, and within which each node represented a topic and each edge indicated the relationship between topics. The index to measure topic association strength defined in this paper reflects both similarity and dissimilarity between topics, which helps us to better obtain the evolution trend, the main evolution path and the content change of topics. In addition, the analysis framework used in this study can reveal more micro topic evolution details. The experimental results show that this analysis framework can be used to track the evolution of research topics at a micro level and the social network analysis method used in this paper can help to grasp the topic evolution path and content changes. However, the analysis framework will produce limited results when conducting unstructured data other than the bibliographic data. In addition, the effectiveness of the framework introduced in this paper needs to be verified in more research fields. Surely, this analysis framework introduced in this paper can help scholars and researchers map the evolution process of research topics and grasp the content changes in a field's research topics over time.

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