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Highly tweeted science articles: who tweets them? An analysis of Twitter user profile descriptions

Author

Listed:
  • Julia Vainio

    (University of Turku)

  • Kim Holmberg

    (University of Turku)

Abstract

In this study we examined who tweeted academic articles that had at least one Finnish author or co-author affiliation and that had high altmetric counts on Twitter. In this investigation of national level altmetrics we chose the most tweeted scientific articles from four broad areas of science (Agricultural, Engineering and Technological Sciences; Medical and Health Sciences; Natural Sciences; Social Sciences and Humanities). By utilizing both quantitative and qualitative methods of analysis, we studied the data using research techniques such as keyword categorization, co-word analysis and content analysis of user profile descriptions. Our results show that contrary to a random sample of Twitter users, users who tweet academic articles describe themselves more factually and by emphasizing their occupational expertise rather than personal interests. The more field-specific the articles were, the more research-related descriptions dominated in Twitter profile descriptions. We also found that scientific articles were tweeted to promote ideological views especially in instances where the article represented a topic that divides general opinion.

Suggested Citation

  • Julia Vainio & Kim Holmberg, 2017. "Highly tweeted science articles: who tweets them? An analysis of Twitter user profile descriptions," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 345-366, July.
    • Handle: RePEc:spr:scient:v:112:y:2017:i:1:d:10.1007_s11192-017-2368-0
    DOI: 10.1007/s11192-017-2368-0
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    References listed on IDEAS

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    1. Lutz Bornmann, 2016. "What do altmetrics counts mean? A plea for content analyses," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(4), pages 1016-1017, April.
    2. Kim Holmberg & Mike Thelwall, 2014. "Disciplinary differences in Twitter scholarly communication," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1027-1042, November.
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    Cited by:

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    4. Kim Holmberg & Juha Hedman & Timothy D. Bowman & Fereshteh Didegah & Mikael Laakso, 2020. "Do articles in open access journals have more frequent altmetric activity than articles in subscription-based journals? An investigation of the research output of Finnish universities," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 645-659, January.
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    10. 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.
    11. Yu, Houqiang & Xiao, Tingting & Xu, Shenmeng & Wang, Yuefen, 2019. "Who posts scientific tweets? An investigation into the productivity, locations, and identities of scientific tweeters," Journal of Informetrics, Elsevier, vol. 13(3), pages 841-855.
    12. Fei Shu & Wen Lou & Stefanie Haustein, 2018. "Can Twitter increase the visibility of Chinese publications?," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 505-519, July.
    13. 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.
    14. Apostolidis, Chrysostomos & Devine, Anthony & Jabbar, Abdul, 2022. "From chalk to clicks – The impact of (rapid) technology adoption on employee emotions in the higher education sector," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    15. Robin Haunschild & Lutz Bornmann, 2021. "Can tweets be used to detect problems early with scientific papers? A case study of three retracted COVID-19/SARS-CoV-2 papers," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5181-5199, June.
    16. Lutz Bornmann & Robin Haunschild & Vanash M Patel, 2020. "Are papers addressing certain diseases perceived where these diseases are prevalent? The proposal to use Twitter data as social-spatial sensors," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-22, November.
    17. Didegah, Fereshteh & Mejlgaard, Niels & Sørensen, Mads P., 2018. "Investigating the quality of interactions and public engagement around scientific papers on Twitter," Journal of Informetrics, Elsevier, vol. 12(3), pages 960-971.

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