Language Bias in the Google Scholar Ranking Algorithm

The visibility of academic articles or conference papers depends on their being easily found in search engines, above all Google Scholar. To enhance this visibility, engine optimization (SEO) has been applied recent years to engines order optimize documents and, thereby, ensure they are better ranked pages (i.e., ASEO). achieve degree optimization, we first need further our understanding Scholar’s relevance ranking algorithm, so that, based knowledge, can highlight improve those characteristics that already present and which taken into account by the algorithm. This study seeks advance knowledge line research determining whether language a document is published positioning factor Scholar Here, employ reverse engineering methodology statistical analysis uses Spearman’s correlation coefficient. results obtained point bias multilingual searches conducted with languages other than English systematically relegated positions make them virtually invisible. finding important repercussions, both for conducting optimizing Scholar, especially critical subjects expressed same way languages, case, example, trademarks, chemical compounds, industrial products, acronyms, drugs, diseases, etc.