Language Bias in the Google Scholar Ranking Algorithm

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.

A systematic review of the state of knowledge on environment and the Belt and Road Initiative

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Overcoming Language Barriers in Academia: Machine Translation Tools and a Vision for a Multilingual Future

“…One concrete manifestation of that is using keywords exclusively in English during literature searches (Pabón Escobar and da Costa 2006 , Kirchik et al 2012 , Liang et al 2013 , Neimann Rasmussen and Montgomery 2018 , Amano et al 2021a ). This effect can be amplified by language biases in search engines (Rovira et al 2021 ). Overlooking non-English studies can result in large gaps within global databases, which affects policy, management, and decision-making (Amano and Sutherland 2013 , Amano et al 2016 , 2021a , Konno et al 2020 , Angulo et al 2021 , Kirpotin et al 2021 ).…”

Lifelines for a drowning science ‐ improving findability and synthesis of hydrologic publications

“…Instead, the large publication numbers force researchers to make judgement calls on where to invest their limited reading time at the risk of being biased (Minx et al, 2017). Rather than systematic reviews (Page et al, 2021), researchers might rely on search engine results (Rovira et al, 2021), recommendations by colleagues, supervisors or on social media (Tenopir et al, 2019), or on what references they see in other articles (Peterson et al, 2010). A consequence of such biased literature sourcing methods is that our reference lists are biased as well.…”