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Mining network-level properties of Twitter altmetrics data

Author

Listed:
  • 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)

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.

Suggested Citation

  • Anwar Said & Timothy D. Bowman & Rabeeh Ayaz Abbasi & Naif Radi Aljohani & Saeed-Ul Hassan & Raheel Nawaz, 2019. "Mining network-level properties of Twitter altmetrics data," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 217-235, July.
    • Handle: RePEc:spr:scient:v:120:y:2019:i:1:d:10.1007_s11192-019-03112-0
    DOI: 10.1007/s11192-019-03112-0
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    References listed on IDEAS

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    Cited by:

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    2. Zhichao Fang & Rodrigo Costas & Paul Wouters, 2022. "User engagement with scholarly tweets of scientific papers: a large-scale and cross-disciplinary analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4523-4546, August.
    3. Sergio Copiello, 2020. "Other than detecting impact in advance, alternative metrics could act as early warning signs of retractions: tentative findings of a study into the papers retracted by PLoS ONE," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2449-2469, December.
    4. Saeed-Ul Hassan & Naif R. Aljohani & Mudassir Shabbir & Umair Ali & Sehrish Iqbal & Raheem Sarwar & Eugenio Martínez-Cámara & Sebastián Ventura & Francisco Herrera, 2020. "Tweet Coupling: a social media methodology for clustering scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 973-991, August.
    5. Yaxue Ma & Zhichao Ba & Yuxiang Zhao & Jin Mao & Gang Li, 2021. "Understanding and predicting the dissemination of scientific papers on social media: a two-step simultaneous equation modeling–artificial neural network approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7051-7085, August.
    6. ↓Xia Peng & Zequan Xiong & Li Yang, 2024. "Can document characteristics affect motivations for literature usage?," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3543-3563, June.
    7. Saeed-Ul Hassan & Sehrish Iqbal & Naif R. Aljohani & Salem Alelyani & Alesia Zuccala, 2020. "Introducing the ‘alt-index’ for measuring the social visibility of scientific research," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(3), pages 1407-1419, June.
    8. Jingfang Liu & Yafei Liu, 2022. "Exploring the User Interaction Network in an Anxiety Disorder Online Community: An Exponential Random Graph Model with Topical and Emotional Effects," IJERPH, MDPI, vol. 19(11), pages 1-13, May.
    9. Weiwei Yan & Qian Liu & Ruoyu Chen & Shengwei Yi, 2020. "Social networks formed by follower–followee relationships on academic social networking sites: an examination of corporation users," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2083-2101, September.
    10. Zhichao Fang & Rodrigo Costas & Wencan Tian & Xianwen Wang & Paul Wouters, 2021. "How is science clicked on Twitter? Click metrics for Bitly short links to scientific publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(7), pages 918-932, July.
    11. Rodrigo Costas & Sarah de Rijcke & Noortje Marres, 2021. "“Heterogeneous couplings”: Operationalizing network perspectives to study science‐society interactions through social media metrics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(5), pages 595-610, May.
    12. Dorte Drongstrup & Shafaq Malik & Naif Radi Aljohani & Salem Alelyani & Iqra Safder & Saeed-Ul Hassan, 2020. "Can social media usage of scientific literature predict journal indices of AJG, SNIP and JCR? An altmetric study of economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1541-1558, November.

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