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Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus

Author

Listed:
  • Cristòfol Rovira

    (Department of Communication, Universitat Pompeu Fabra, 08002 Barcelona, Spain)

  • Lluís Codina

    (Department of Communication, Universitat Pompeu Fabra, 08002 Barcelona, Spain)

  • Frederic Guerrero-Solé

    (Department of Communication, Universitat Pompeu Fabra, 08002 Barcelona, Spain)

  • Carlos Lopezosa

    (Department of Communication, Universitat Pompeu Fabra, 08002 Barcelona, Spain)

Abstract

Search engine optimization (SEO) constitutes the set of methods designed to increase the visibility of, and the number of visits to, a web page by means of its ranking on the search engine results pages. Recently, SEO has also been applied to academic databases and search engines, in a trend that is in constant growth. This new approach, known as academic SEO (ASEO), has generated a field of study with considerable future growth potential due to the impact of open science. The study reported here forms part of this new field of analysis. The ranking of results is a key aspect in any information system since it determines the way in which these results are presented to the user. The aim of this study is to analyze and compare the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms. Specifically, we analyze two search engines and two bibliographic databases: Google Scholar and Microsoft Academic, on the one hand, and Web of Science and Scopus, on the other. A reverse engineering methodology is employed based on the statistical analysis of Spearman’s correlation coefficients. The results indicate that the ranking algorithms used by Google Scholar and Microsoft are the two that are most heavily influenced by citations received. Indeed, citation counts are clearly the main SEO factor in these academic search engines. An unexpected finding is that, at certain points in time, Web of Science (WoS) used citations received as a key ranking factor, despite the fact that WoS support documents claim this factor does not intervene.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:9:p:202-:d:268805
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    References listed on IDEAS

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    Cited by:

    1. Andreas Veglis & Dimitrios Giomelakis, 2019. "Search Engine Optimization," Future Internet, MDPI, vol. 12(1), pages 1-2, December.
    2. Sandro Serpa & Maria José Sá & Ana Isabel Santos & Carlos Miguel Ferreira, 2020. "Challenges for the Academic Editor in the Scientific Publication," Academic Journal of Interdisciplinary Studies, Richtmann Publishing Ltd, vol. 9, May.
    3. Tamás Stadler & Ágoston Temesi & Zoltán Lakner, 2022. "Soil Chemical Pollution and Military Actions: A Bibliometric Analysis," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    4. Andreas Giannakoulopoulos & Nikos Konstantinou & Dimitris Koutsompolis & Minas Pergantis & Iraklis Varlamis, 2019. "Academic Excellence, Website Quality, SEO Performance: Is there a Correlation?," Future Internet, MDPI, vol. 11(11), pages 1-25, November.
    5. Cristòfol Rovira & Lluís Codina & Carlos Lopezosa, 2021. "Language Bias in the Google Scholar Ranking Algorithm," Future Internet, MDPI, vol. 13(2), pages 1-17, January.
    6. Laura Icela González-Pérez & María Soledad Ramírez-Montoya & Francisco José García-Peñalvo, 2021. "Improving Institutional Repositories through User-Centered Design: Indicators from a Focus Group," Future Internet, MDPI, vol. 13(11), pages 1-19, November.
    7. Goran Matošević & Jasminka Dobša & Dunja Mladenić, 2021. "Using Machine Learning for Web Page Classification in Search Engine Optimization," Future Internet, MDPI, vol. 13(1), pages 1-20, January.

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