Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network

Fang Zhang and Shengli Wu ()
Additional contact information
Fang Zhang: Jiangsu University
Shengli Wu: Jiangsu University

Scientometrics, 2021, vol. 126, issue 8, No 36, 7197-7222

Abstract: Abstract Evaluating the impact of papers, researchers and venues objectively is of great significance to academia and beyond. This may help researchers, research organizations, and government agencies in various ways, such as helping researchers find valuable papers and authoritative venues and helping research organizations identify good researchers. A few studies find that rather than treating citations equally, differentiating them is a promising way for impact evaluation of academic entities. However, most of those methods are metadata-based only and do not consider contents of cited and citing papers; while a few content-based methods are not sophisticated, and further improvement is possible. In this paper, we study the citation relationships between entities by content-based approaches. Especially, an ensemble learning method is used to classify citations into different strength types, and a word-embedding based method is used to estimate topical similarity of the citing and cited papers. A heterogeneous network is constructed with the weighted citation links and several other features. Based on the heterogeneous network that consists of three types of entities, we apply an iterative PageRank-like method to rank the impact of papers, authors and venues at the same time through mutual reinforcement. Experiments are conducted on an ACL dataset, and the results demonstrate that our method greatly outperforms state-of-the art competitors in improving ranking effectiveness of papers, authors and venues, as well as in being robust against malicious manipulation of citations.

Keywords: Scientific impact evaluation; Heterogeneous network; Content-based citation analysis; Citation strength; Topical similarity (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-021-04063-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:126:y:2021:i:8:d:10.1007_s11192-021-04063-1

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-021-04063-1

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().