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.
Bibliographical noteFunding Information:
This work was supported by National Research Foundation of Korea Grant funded by the Korean Government ( NRF-2012-2012S1A3A2033291 ) and by the National Research Foundation of Korea (NRF) Grant funded by the Korea Government (No. 2012033242 ).
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Library and Information Sciences