Mining network-level properties of Twitter altmetrics data

Anwar Said (), Timothy D. Bowman (), Rabeeh Ayaz Abbasi (), Naif Radi Aljohani (), Saeed-Ul Hassan () and Raheel Nawaz ()
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Anwar Said: Information Technology University
Timothy D. Bowman: Wayne State University
Rabeeh Ayaz Abbasi: Quaid-i-Azam University
Naif Radi Aljohani: King Abdulaziz University
Saeed-Ul Hassan: Information Technology University
Raheel Nawaz: Manchester Metropolitan University

Scientometrics, 2019, vol. 120, issue 1, No 11, 217-235

Abstract: Abstract Social networking sites play a significant role in altmetrics. While 90% of all altmetric mentions come from Twitter, the known microscopic and macroscopic properties of Twitter altmetrics data are limited. In this study, we present a large-scale analysis of Twitter altmetrics data using social network analysis techniques on the ‘mention’ network of Twitter users. Exploiting the network-level properties of over 1.4 million tweets, corresponding to 77,757 scholarly articles, this study focuses on the following aspects of Twitter altmetrics data: (a) the influence of organizational accounts; (b) the formation of disciplinary communities; (c) the cross-disciplinary interaction among Twitter users; (d) the network motifs of influential Twitter users; and (e) testing the small-world property. The results show that Twitter-based social media communities have unique characteristics, which may affect social media usage counts either directly or indirectly. Therefore, instead of treating altmetrics data as a black box, the underlying social media networks, which may either inflate or deflate social media usage counts, need further scrutiny.

Keywords: Altmetrics; Community structure; Influential users; Motifs; Overlapping communities; Twitter (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (12)

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DOI: 10.1007/s11192-019-03112-0

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