Content-based author co-citation analysis

• This study proposes a content-based author co-citation analysis (ACA) method for measuring the similarity between co-cited authors by considering author's citation content.
• We extract citing sentences from the full-text articles in the information science domain and calculate the author similarity between two authors using citing sentence similarity.
• We compare our method with traditional ACA and analyze the differences between traditional ACA and our proposed content-based ACA.
• The results show that our approach provides more details about the sub-disciplines in the domain than with traditional ACA.

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.