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Microsoft Academic automatic document searches: Accuracy for journal articles and suitability for citation analysis

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  • Thelwall, Mike

Abstract

Microsoft Academic is a free academic search engine and citation index that is similar to Google Scholar but can be automatically queried. Its data is potentially useful for bibliometric analysis if it is possible to search effectively for individual journal articles. This article compares different methods to find journal articles in its index by searching for a combination of title, authors, publication year and journal name and uses the results for the widest published correlation analysis of Microsoft Academic citation counts for journal articles so far. Based on 126,312 articles from 323 Scopus subfields in 2012, the optimal strategy to find articles with DOIs is to search for them by title and filter out those with incorrect DOIs. This finds 90% of journal articles. For articles without DOIs, the optimal strategy is to search for them by title and then filter out matches with dissimilar metadata. This finds 89% of journal articles, with an additional 1% incorrect matches. The remaining articles seem to be mainly not indexed by Microsoft Academic or indexed with a different language version of their title. From the matches, Scopus citation counts and Microsoft Academic counts have an average Spearman correlation of 0.95, with the lowest for any single field being 0.63. Thus, Microsoft Academic citation counts are almost universally equivalent to Scopus citation counts for articles that are not recent but there are national biases in the results.

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  • 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.
    • Handle: RePEc:eee:infome:v:12:y:2018:i:1:p:1-9
    DOI: 10.1016/j.joi.2017.11.001
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    References listed on IDEAS

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    Cited by:

    1. Iman Tahamtan & Lutz Bornmann, 2019. "What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1635-1684, December.
    2. Kaiwen Shi & Kan Liu & Xinyan He, 2024. "Heterogeneous hypergraph learning for literature retrieval based on citation intents," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4167-4188, July.
    3. 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.
    4. Thelwall, Mike, 2018. "Dimensions: A competitor to Scopus and the Web of Science?," Journal of Informetrics, Elsevier, vol. 12(2), pages 430-435.
    5. Michael Thelwall, 2018. "Can Microsoft Academic be used for citation analysis of preprint archives? The case of the Social Science Research Network," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 913-928, May.
    6. Alberto Martín-Martín & Mike Thelwall & Enrique Orduna-Malea & Emilio Delgado López-Cózar, 2021. "Google Scholar, Microsoft Academic, Scopus, Dimensions, Web of Science, and OpenCitations’ COCI: a multidisciplinary comparison of coverage via citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 871-906, January.
    7. Michael Gusenbauer, 2019. "Google Scholar to overshadow them all? Comparing the sizes of 12 academic search engines and bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 177-214, January.
    8. 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.
    9. 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.

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