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Title:Topic-based author cocitation analysis: A preliminary exploration
Author(s):Bu, Yi; Huang, Win-bin; Ding, Ying; Ai, Peng
Subject(s):Author cocitation analysis
Topic modeling
Cocitation analysis
Citation analysis
Bibliometrics
Abstract:Author cocitation analysis (ACA) plays a significant role in mapping knowledge domains. However, it has been criticized to be relatively less informative because topic- and semantic-level information of citations has seldom been integrated into ACA. This poster aims to improve the traditional ACA by combining topical information of cocited authors with author cocited counts, which is called topic-based ACA. Author-Conference-Topic (ACT) model is adopted in this research to calculate topic distributions of authors. Compared with traditional ACA, topic-based ACA shows a better clustering ability in visualization and mines more details in knowledge domain mappings.
Issue Date:2017
Publisher:iSchools
Citation Info:Bu, Y., Huang, W., Ding, Y., & Ai, P. (2017). Topic-based Author Cocitation Analysis: A Preliminary Exploration. In iConference 2017 Proceedings (pp. 609-612). https://doi.org/10.9776/17302
Series/Report:iConference 2017 Proceedings
Genre:Conference Poster
Type:Text
Language:English
URI:http://hdl.handle.net/2142/96700
DOI:https://doi.org/10.9776/17302
Rights Information:Copyright 2017 Yi Bu, Win-bin Huang, Ying Ding, and Peng Ai
Date Available in IDEALS:2017-07-27


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