Content-based author co-citation analysis |
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| Institution: | 1. Department of Library and Information Science, Yonsei University, Seoul, Republic of Korea;2. Department of Information and Library Science, Indiana University, Bloomington, IN, USA;1. School of Information Management, Wuhan University, Luojia Shan, Wuhan, Hubei Province 430072, PR China;2. School of Information and Safety Engineering, Zhongnan University of Economics and Law, 182# Nanhu Avenue, East Lake High-tech Development Zone, Wuhan 430073, PR China;1. Centre for Research & Development Monitoring (ECOOM), University of Antwerp, Middelheimlaan 1, 2020 Antwerp, Belgium;2. Department of Research Affairs and Centre for Research & Development Monitoring (ECOOM), University of Antwerp, Middelheimlaan 1, 2020 Antwerp, Belgium;3. Antwerp Maritime Academy, Noordkasteel-Oost 6, 2030 Antwerp, Belgium;1. Rathenau Institute, Science System Assessment, Anna van Saksenlaan 51, 2593 HW The Hague, The Netherlands;2. VU University Amsterdam, Network Institute & Department of Organization Science, De Boelelaan 1105, Amsterdam, The Netherlands;3. GRIPS – National Graduate Institute for Policy Studies, 7-22-1 Roppongi, Minato-ku, Tokyo 106-867, Japan;4. Université Paris-Est, ESIEE – LATTS – IFRIS, 2, bd Blaise Pascal, Noisy le Grand 93160, France;5. CNRS – Aix-Marseille Université, LEST UMR 7317, 35 Avenue Jules Ferry, 13626 Aix en Provence Cedex 01, France;1. Science Education Center, National Taiwan Normal University, No. 88, 4th Section, Ting-Chou Road, Wen-Shan District, Taipei City 11677, Taiwan, ROC;2. Work-based Education Research Centre (WERC), Victoria Institute for Education, Diversity and Lifelong Learning, Victoria University, PO Box 14428, Melbourne, VIC 8001, Australia |
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| Abstract: | Author co-citation analysis (ACA) has long been used as an effective method for identifying the intellectual structure of a research domain, but it relies on simple co-citation counting, which does not take the citation content into consideration. The present study proposes a new method for measuring the similarity between co-cited authors by considering author's citation content. We collected the full-text journal articles in the information science domain and extracted the citing sentences to calculate their similarity distances. We compared our method with traditional ACA and found out that our approach, while displaying a similar intellectual structure for the information science domain as the other baseline methods, also provides more details about the sub-disciplines in the domain than with traditional ACA. |
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| Keywords: | Author co-citation analysis Citation content analysis Bibliometrics Information science Citation analysis |
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