Language bias in the Google Scholar ranking algorithm
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- dc.contributor.author Rovira, Cristòfol
- dc.contributor.author Codina, Lluís
- dc.contributor.author Lopezosa, Carlos
- dc.date.accessioned 2021-03-17T08:44:45Z
- dc.date.available 2021-03-17T08:44:45Z
- dc.date.issued 2021
- dc.description.abstract The visibility of academic articles or conference papers depends on their being easily found in academic search engines, above all in Google Scholar. To enhance this visibility, search engine optimization (SEO) has been applied in recent years to academic search engines in order to optimize documents and, thereby, ensure they are better ranked in search pages (i.e., academic search engine optimization or ASEO). To achieve this degree of optimization, we first need to further our understanding of Google Scholar’s relevance ranking algorithm, so that, based on this knowledge, we can highlight or improve those characteristics that academic documents already present and which are taken into account by the algorithm. This study seeks to advance our knowledge in this line of research by determining whether the language in which a document is published is a positioning factor in the Google Scholar relevance ranking algorithm. Here, we employ a reverse engineering research methodology based on a statistical analysis that uses Spearman’s correlation coefficient. The results obtained point to a bias in multilingual searches conducted in Google Scholar with documents published in languages other than in English being systematically relegated to positions that make them virtually invisible. This finding has important repercussions, both for conducting searches and for optimizing positioning in Google Scholar, being especially critical for articles on subjects that are expressed in the same way in English and other languages, the case, for example, of trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.en
- dc.description.sponsorship This work is part of the Project “Interactive storytelling and digital visibility in interactive documentary and structured journalism”. RTI2018-095714-B-C21 (Micinn/Feder) Ministry of Science, Innovation and Universities of Spain.
- dc.format.mimetype application/pdf
- dc.identifier.citation Rovira C, Codina L, Lopezosa C. Language bias in the Google Scholar ranking algorithm. Future Internet. 2021;13(2):31. DOI: 10.3390/fi13020031
- dc.identifier.doi http://dx.doi.org/10.3390/fi13020031
- dc.identifier.issn 1999-5903
- dc.identifier.uri http://hdl.handle.net/10230/46813
- dc.language.iso eng
- dc.publisher MDPI
- dc.relation.ispartof Future Internet. 2021;13(2):31
- dc.relation.projectID info:eu-repo/grantAgreement/ES/2PE/RTI2018-095714-B-C21
- dc.rights © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.keyword ASEO
- dc.subject.keyword SEO
- dc.subject.keyword Reverse engineering
- dc.subject.keyword Citations
- dc.subject.keyword Google scholar
- dc.subject.keyword Algorithms
- dc.subject.keyword Relevance ranking
- dc.subject.keyword Relevance ranking
- dc.subject.keyword Citation databases
- dc.subject.keyword Academic search engines
- dc.subject.keyword Multilingual search
- dc.title Language bias in the Google Scholar ranking algorithmen
- dc.type info:eu-repo/semantics/article
- dc.type.version info:eu-repo/semantics/publishedVersion