Tweet Coupling: a social media methodology for clustering scientific publications.

Autor/inn/en	Hassan, Saeed-Ul; Aljohani, Naif R.; Shabbir, Mudassir; Ali, Umair; Iqbal, Sehrish; Sarwar, Raheem; Martínez-Cámara, Eugenio; Ventura, Sebastián; Herrera, Francisco
Titel	Tweet Coupling: a social media methodology for clustering scientific publications.
Quelle	In: Scientometrics, (2020) 2, S.973-991 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-020-03499-1
Schlagwörter	Scientific document clustering; Social media; Altmetrics; Tweet Coupling; Bibliographic coupling
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.
Erfasst von	OLC
Update	2023/2/05