Tweet Coupling: a social media methodology for clustering scientific publications

Saeed-Ul Hassan (), Naif R. Aljohani (), Mudassir Shabbir (), Umair Ali (), Sehrish Iqbal (), Raheem Sarwar (), Eugenio Martínez-Cámara (), Sebastián Ventura () and Francisco Herrera ()
Additional contact information
Saeed-Ul Hassan: Information Technology University
Naif R. Aljohani: King Abdulaziz University
Mudassir Shabbir: Information Technology University
Umair Ali: Information Technology University
Sehrish Iqbal: Information Technology University
Raheem Sarwar: Information Technology University
Eugenio Martínez-Cámara: Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI), University of Granada
Sebastián Ventura: King Abdulaziz University
Francisco Herrera: King Abdulaziz University

Scientometrics, 2020, vol. 124, issue 2, No 8, 973-991

Abstract: Abstract We argue that classic citation-based scientific document clustering approaches, like co-citation or Bibliographic Coupling, lack to leverage the social-usage of the scientific literature originate through online information dissemination platforms, such as Twitter. In this paper, we present the methodology Tweet Coupling, which measures the similarity between two or more scientific documents if one or more Twitter users mention them in the tweet(s). We evaluate our proposal on an altmetric dataset, which consists of 3081 scientific documents and 8299 unique Twitter users. By employing the clustering approaches of Bibliographic Coupling and Tweet Coupling, we find the relationship between the bibliographic and tweet coupled scientific documents. Further, using VOSviewer, we empirically show that Tweet Coupling appears to be a better clustering methodology to generate cohesive clusters since it groups similar documents from the subfields of the selected field, in contrast to the Bibliographic Coupling approach that groups cross-disciplinary documents in the same cluster.

Keywords: Scientific document clustering; Social media; Altmetrics; Tweet Coupling; Bibliographic coupling (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-020-03499-1 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:124:y:2020:i:2:d:10.1007_s11192-020-03499-1

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

DOI: 10.1007/s11192-020-03499-1

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 ().