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How do academic topics shift across altmetric sources? A case study of the research area of Big Data

Author

Listed:
  • Xiaozan Lyu

    (Zhejiang University
    Leiden University)

  • Rodrigo Costas

    (Leiden University
    Stellenbosch University)

Abstract

Taking the research area of Big Data as a case study, we propose an approach for exploring how academic topics shift through the interactions among audiences across different altmetric sources. Data used is obtained from Web of Science and Altmetric.com, with a focus on Blog, News, Policy, Wikipedia, and Twitter. Author keywords from publications and terms from online events are extracted as the main topics of the publications and the online discussion of their audiences at Altmetric. Different measures are applied to determine the (dis)similarities between the topics put forward by the publication authors and those by the online audiences. Results show that overall there are substantial differences between the two sets of topics around Big Data scientific research. The main exception is Twitter, where high-frequency hashtags in tweets have a stronger concordance with the author keywords in publications. Among the online communities, Blogs and News show a strong similarity in the terms commonly used, while Policy documents and Wikipedia articles exhibit the strongest dissimilarity in considering and interpreting Big Data related research. Specifically, the audiences not only focus on more easy-to-understand academic topics related to social or general issues, but also extend them to a broader range of topics in their online discussions. This study lays the foundations for further investigations about the role of online audiences in the transformation of academic topics across altmetric sources, and the degree of concern and reception of scholarly contents by online communities.

Suggested Citation

  • Xiaozan Lyu & Rodrigo Costas, 2020. "How do academic topics shift across altmetric sources? A case study of the research area of Big Data," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 909-943, May.
    • Handle: RePEc:spr:scient:v:123:y:2020:i:2:d:10.1007_s11192-020-03415-7
    DOI: 10.1007/s11192-020-03415-7
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    Cited by:

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    2. Ola G. El‐Taliawi & Nihit Goyal & Michael Howlett, 2021. "Holding out the promise of Lasswell's dream: Big data analytics in public policy research and teaching," Review of Policy Research, Policy Studies Organization, vol. 38(6), pages 640-660, November.
    3. 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.
    4. 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.
    5. Alba Viana Lora & Marta Gemma Nel-lo Andreu, 2020. "Alternative Metrics for Assessing the Social Impact of Tourism Research," Sustainability, MDPI, vol. 12(10), pages 1-12, May.

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    More about this item

    Keywords

    Altmetrics; Topic shift; Similarity measurements; Big data research area;
    All these keywords.

    JEL classification:

    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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