Yet another method for author co‐citation analysis: A new approach based on paragraph similarity. Issue 1 (24th October 2017)

• Record Type: Journal Article
• Authors: Hsiao, Tsung‐Ming; Chen, Kuang‐hua
• Is Part Of: Proceedings of the Association for Information Science and Technology. Volume 54:Issue 1(2017)
• Page Start: 170
• Page End: 178
• Publication Date: 2017-10-24
• Subjects: author co‐citation analysis -- bibliometrics -- citation analysis; Information science -- Congresses; Information technology -- Congresses; Information science; Information technology; Conference papers and proceedings; 020
• DOI: 10.1002/pra2.2017.14505401019
• Languages: English
• ISSNs: 2373-9231

Abstract

ABSTRACT: Co‐citation analysis has been widely adopted to represent the intellectual structure of a discipline. In general, all co‐cited author pairs are regarded equal. With the development of computer technology and easy accessibility of machine readable full‐text articles, new weighting schemes for measuring co‐citation strength (CCS) have been proposed. However, in previous studies, only distance and sentence similarity are used to adjust CCS when applying co‐citation analysis. In this study, we propose a new approach to measuring CCS based on paragraph similarity. Investigation was carried out to compare our approach and traditional author co‐citation analysis (TACA), as well as other different parametric ACAs. Preliminary results show that TACA and distance‐based ACA (DACA) share many commonalities. In contrast, similarity‐based ACAs reveal the different structure from that of TACA and DACA. However, differences in resulting network structure were still found between paragraph‐similarity‐based ACA (PACA) and sentence‐similarity‐based ACA (SACA). Compared to SACA, PACA produces less number of factors and clusters and moderate size of clusters.