# Language Bias in the Google Scholar Ranking Algorithm
## 1 Introduction
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.