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Mining network-level properties of Twitter altmetrics data

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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.

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Notes

  1. https://www.researchgate.net.

  2. http://theinf1.informatik.uni-jena.de/motifs/.

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Acknowledgements

The research has been supported by the National Research Programme for Universities Grant No. 6857/Punjab/NRPU/R&D/HEC/2016, funded by the Higher Education Commission of Pakistan, with Dr. Saeed Ul Hassan as Principal Investigator.

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Authors and Affiliations

  1. Information Technology University, 346-B, Ferozepur Road, Lahore, Pakistan

    Anwar Said & Saeed-Ul Hassan

  2. School of Information Sciences, Wayne State University, Detroit, MI, USA

    Timothy D. Bowman

  3. Department of Computer Science, Quaid-i-Azam University, Islamabad, Pakistan

    Rabeeh Ayaz Abbasi

  4. Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia

    Naif Radi Aljohani

  5. Department of Operations, Technology, Events and Hospitality Management, Manchester Metropolitan University, Manchester, UK

    Raheel Nawaz

Authors
  1. Anwar Said
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  2. Timothy D. Bowman
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  3. Rabeeh Ayaz Abbasi
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  4. Naif Radi Aljohani
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  5. Saeed-Ul Hassan
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  6. Raheel Nawaz
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Corresponding author

Correspondence to Saeed-Ul Hassan.

Appendix

Appendix

This section presents the top 5 influential users from each community with their fields and types. Field attribute represents the subject-field or domain of the user, which helps to understand the community structure of the network. Note that subject-field and/or domain is manually extracted from the Twitter users’ profiles to properly label the influential users. There are eight major communities which cover more than 98% of users of the whole network (Table 3).

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Said, A., Bowman, T.D., Abbasi, R.A. et al. Mining network-level properties of Twitter altmetrics data. Scientometrics 120, 217–235 (2019). https://doi.org/10.1007/s11192-019-03112-0

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