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Article

Language Bias in the Google Scholar Ranking Algorithm

1
Department of Communication, Universitat Pompeu Fabra, 08002 Barcelona, Spain
2
UPF Barcelona School of Management, Balmes, 134, 08008 Barcelona, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Fatos Xhafa
Future Internet 2021, 13(2), 31; https://doi.org/10.3390/fi13020031
Received: 30 December 2020 / Revised: 17 January 2021 / Accepted: 23 January 2021 / Published: 27 January 2021
(This article belongs to the Special Issue The Current State of Search Engines and Search Engine Optimization)
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. View Full-Text
Keywords: ASEO; SEO; reverse engineering; citations; google scholar; algorithms; relevance ranking; citation databases; academic search engines; multilingual search ASEO; SEO; reverse engineering; citations; google scholar; algorithms; relevance ranking; citation databases; academic search engines; multilingual search
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MDPI and ACS Style

Rovira, C.; Codina, L.; Lopezosa, C. Language Bias in the Google Scholar Ranking Algorithm. Future Internet 2021, 13, 31. https://doi.org/10.3390/fi13020031

AMA Style

Rovira C, Codina L, Lopezosa C. Language Bias in the Google Scholar Ranking Algorithm. Future Internet. 2021; 13(2):31. https://doi.org/10.3390/fi13020031

Chicago/Turabian Style

Rovira, Cristòfol, Lluís Codina, and Carlos Lopezosa. 2021. "Language Bias in the Google Scholar Ranking Algorithm" Future Internet 13, no. 2: 31. https://doi.org/10.3390/fi13020031

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