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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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    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.
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