Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches.

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Bibliographic Details
Title:	Measuring author research relatedness: A comparison of word-based, topic-based, and author cocitation approaches.
Authors:	Lu, Kun¹, Wolfram, Dietmar¹
Source:	Journal of the American Society for Information Science & Technology. Oct2012, Vol. 63 Issue 10, p1973-1986. 14p.
Subjects:	Authors, Authorship, Bibliometrics, Information retrieval, Information science, Scholarly method, Serial publications, Citation analysis, Data analysis software
Abstract:	Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	Relationships between authors based on characteristics of published literature have been studied for decades. Author cocitation analysis using mapping techniques has been most frequently used to study how closely two authors are thought to be in intellectual space based on how members of the research community co-cite their works. Other approaches exist to study author relatedness based more directly on the text of their published works. In this study we present static and dynamic word-based approaches using vector space modeling, as well as a topic-based approach based on latent Dirichlet allocation for mapping author research relatedness. Vector space modeling is used to define an author space consisting of works by a given author. Outcomes for the two word-based approaches and a topic-based approach for 50 prolific authors in library and information science are compared with more traditional author cocitation analysis using multidimensional scaling and hierarchical cluster analysis. The two word-based approaches produced similar outcomes except where two authors were frequent co-authors for the majority of their articles. The topic-based approach produced the most distinctive map. [ABSTRACT FROM AUTHOR]
ISSN:	15322882
DOI:	10.1002/asi.22628