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Citation analysis with microsoft academic

Author

Listed:
  • Sven E. Hug

    (ETH Zurich
    University of Zurich)

  • Michael Ochsner

    (ETH Zurich
    University of Lausanne)

  • Martin P. Brändle

    (University of Zurich
    University of Zurich)

Abstract

We explore if and how Microsoft Academic (MA) could be used for bibliometric analyses. First, we examine the Academic Knowledge API (AK API), an interface to access MA data, and compare it to Google Scholar (GS). Second, we perform a comparative citation analysis of researchers by normalizing data from MA and Scopus. We find that MA offers structured and rich metadata, which facilitates data retrieval, handling and processing. In addition, the AK API allows retrieving frequency distributions of citations. We consider these features to be a major advantage of MA over GS. However, we identify four main limitations regarding the available metadata. First, MA does not provide the document type of a publication. Second, the “fields of study” are dynamic, too specific and field hierarchies are incoherent. Third, some publications are assigned to incorrect years. Fourth, the metadata of some publications did not include all authors. Nevertheless, we show that an average-based indicator (i.e. the journal normalized citation score; JNCS) as well as a distribution-based indicator (i.e. percentile rank classes; PR classes) can be calculated with relative ease using MA. Hence, normalization of citation counts is feasible with MA. The citation analyses in MA and Scopus yield uniform results. The JNCS and the PR classes are similar in both databases, and, as a consequence, the evaluation of the researchers’ publication impact is congruent in MA and Scopus. Given the fast development in the last year, we postulate that MA has the potential to be used for full-fledged bibliometric analyses.

Suggested Citation

  • Sven E. Hug & Michael Ochsner & Martin P. Brändle, 2017. "Citation analysis with microsoft academic," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 371-378, April.
    • Handle: RePEc:spr:scient:v:111:y:2017:i:1:d:10.1007_s11192-017-2247-8
    DOI: 10.1007/s11192-017-2247-8
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    References listed on IDEAS

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    1. Bornmann, Lutz & Leydesdorff, Loet & Mutz, Rüdiger, 2013. "The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits," Journal of Informetrics, Elsevier, vol. 7(1), pages 158-165.
    2. Anne-Wil Harzing, 2016. "Microsoft Academic (Search): a Phoenix arisen from the ashes?," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(3), pages 1637-1647, September.
    3. Anne-Wil Harzing & Satu Alakangas & David Adams, 2014. "hIa: an individual annual h-index to accommodate disciplinary and career length differences," Scientometrics, Springer;Akadémiai Kiadó, vol. 99(3), pages 811-821, June.
    4. Robert J. W. Tijssen & Martijn S. Visser & Thed N. van Leeuwen, 2002. "Benchmarking international scientific excellence: Are highly cited research papers an appropriate frame of reference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(3), pages 381-397, July.
    5. Ad A.M. Prins & Rodrigo Costas & Thed N. van Leeuwen & Paul F. Wouters, 2016. "Using Google Scholar in research evaluation of humanities and social science programs: A comparison with Web of Science data," Research Evaluation, Oxford University Press, vol. 25(3), pages 264-270.
    6. Ludo Waltman & Michael Schreiber, 2013. "On the calculation of percentile-based bibliometric indicators," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(2), pages 372-379, February.
    7. Lutz Bornmann & Andreas Thor & Werner Marx & Hermann Schier, 2016. "The application of bibliometrics to research evaluation in the humanities and social sciences: An exploratory study using normalized Google Scholar data for the publications of a research institute," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(11), pages 2778-2789, November.
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    Citations

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    Cited by:

    1. Lutz Bornmann & Robin Haunschild & Sven E. Hug, 2018. "Visualizing the context of citations referencing papers published by Eugene Garfield: a new type of keyword co-occurrence analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 427-437, February.
    2. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "Globalised vs averaged: Bias and ranking performance on the author level," Journal of Informetrics, Elsevier, vol. 13(1), pages 299-313.
    3. Nisar Ali & Zahid Halim & Syed Fawad Hussain, 2023. "An artificial intelligence-based framework for data-driven categorization of computer scientists: a case study of world’s Top 10 computing departments," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1513-1545, March.
    4. Jung, Sukhwan & Segev, Aviv, 2022. "DAC: Descendant-aware clustering algorithm for network-based topic emergence prediction," Journal of Informetrics, Elsevier, vol. 16(3).
    5. Thelwall, Mike, 2018. "Microsoft Academic automatic document searches: Accuracy for journal articles and suitability for citation analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 1-9.
    6. Zhentao Liang & Jin Mao & Kun Lu & Gang Li, 2021. "Finding citations for PubMed: a large-scale comparison between five freely available bibliographic data sources," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9519-9542, December.
    7. Cristòfol Rovira & Lluís Codina & Frederic Guerrero-Solé & Carlos Lopezosa, 2019. "Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus," Future Internet, MDPI, vol. 11(9), pages 1-21, September.
    8. Xiancheng Li & Wenge Rong & Haoran Shi & Jie Tang & Zhang Xiong, 2018. "The impact of conference ranking systems in computer science: a comparative regression analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 879-907, August.
    9. Lutz Bornmann & K. Brad Wray & Robin Haunschild, 2020. "Citation concept analysis (CCA): a new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by exemplary case studies including classic books by Thomas S. K," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(2), pages 1051-1074, February.
    10. Robin Haunschild & Sven E. Hug & Martin P. Brändle & Lutz Bornmann, 2018. "The number of linked references of publications in Microsoft Academic in comparison with the Web of Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(1), pages 367-370, January.
    11. Dunaiski, Marcel & Geldenhuys, Jaco & Visser, Willem, 2019. "On the interplay between normalisation, bias, and performance of paper impact metrics," Journal of Informetrics, Elsevier, vol. 13(1), pages 270-290.
    12. Kousha, Kayvan & Thelwall, Mike & Abdoli, Mahshid, 2018. "Can Microsoft Academic assess the early citation impact of in-press articles? A multi-discipline exploratory analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 287-298.
    13. Martin Wieland & Juan Gorraiz, 2020. "The rivalry between Bernini and Borromini from a scientometric perspective," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1643-1663, November.
    14. Klaus Kammerer & Manuel Göster & Manfred Reichert & Rüdiger Pryss, 2021. "Ambalytics: A Scalable and Distributed System Architecture Concept for Bibliometric Network Analyses," Future Internet, MDPI, vol. 13(8), pages 1-29, August.
    15. Anne-Wil Harzing, 2019. "Two new kids on the block: How do Crossref and Dimensions compare with Google Scholar, Microsoft Academic, Scopus and the Web of Science?," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 341-349, July.
    16. Jung, Sukhwan & Yoon, Wan Chul, 2020. "An alternative topic model based on Common Interest Authors for topic evolution analysis," Journal of Informetrics, Elsevier, vol. 14(3).
    17. Kousha, Kayvan & Thelwall, Mike, 2018. "Can Microsoft Academic help to assess the citation impact of academic books?," Journal of Informetrics, Elsevier, vol. 12(3), pages 972-984.
    18. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    19. Thelwall, Mike, 2018. "Dimensions: A competitor to Scopus and the Web of Science?," Journal of Informetrics, Elsevier, vol. 12(2), pages 430-435.
    20. Tahamtan, Iman & Bornmann, Lutz, 2018. "Core elements in the process of citing publications: Conceptual overview of the literature," Journal of Informetrics, Elsevier, vol. 12(1), pages 203-216.
    21. van der Wouden, Frank & Youn, Hyejin, 2023. "The impact of geographical distance on learning through collaboration," Research Policy, Elsevier, vol. 52(2).
    22. Vaccario, Giacomo & Medo, Matúš & Wider, Nicolas & Mariani, Manuel Sebastian, 2017. "Quantifying and suppressing ranking bias in a large citation network," Journal of Informetrics, Elsevier, vol. 11(3), pages 766-782.

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