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Tweet coupling: a social media methodology for clustering scientific publications
Hassan, SU Aljohani, Naif Radi Shabbir, M Ali, U Iqbal, S Sarwar, R Martínez-Cámara, E Ventura, S Herrera, F
Hassan, SU
Aljohani, Naif Radi
Shabbir, M
Ali, U
Iqbal, S
Sarwar, R
Martínez-Cámara, E
Ventura, S
Herrera, F
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2020-05-18
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Abstract
© 2020, Akadémiai Kiadó, Budapest, Hungary. 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.
Citation
Hassan, S., Aljohani, N.R., Shabbir, M., Ali, U., Iqbal, S., Sarwar, R., Martínez-Cámara, E., Ventura, S. and Herrera, F. (2020) Tweet coupling: a social media methodology for clustering scientific publications, Scientometrics, 124, pp. 973–991.
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Journal article
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en
Description
This is an accepted manuscript of an article published by Springer in Scientometrics on 18/05/2020, available online: https://doi.org/10.1007/s11192-020-03499-1
The accepted version of the publication may differ from the final published version.
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0138-9130
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1588-2861
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The authors (Saeed-Ul Hassan & Mudassir Shabbir) were funded by the CIPL (National Center in Big Data and Cloud Computing (NCBC) grant, received from the Planning Commission of Pakistan, through Higher Education Commission (HEC) of Pakistan. This work was partially supported by the Spanish Ministry of Science and Technology under the projects TIN2017-89517-P and TIN2017-83445-P. Eugenio Martínez Cámara was supported by the Spanish Government Programme Juan de la Cierva Incorporación (IJC2018-036092-I).