.png){kind=icon}
.png){kind=icon}
Return
Content-based author co-citation analysis
.png){kind=icon}
PRE
AI
DOI：10.1016/j.joi.2013.12.001.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Author co-citation analysis
Citation content analysis
Bibliometrics
Information science
Citation analysis
Journal
IF:
3.5
Papers:
1.6K
Citations:
7.9K