Academic Search Engine Optimization (ASEO)

2010 ◽  
Vol 41 (2) ◽  
pp. 176-190 ◽  
Author(s):  
Jöran Beel ◽  
Bela Gipp ◽  
Erik Wilde
Keyword(s):  
Search Engine ◽  
Search Engine Optimization ◽  
Academic Search

scholarly journals Language Bias in the Google Scholar Ranking Algorithm

2021 ◽  
Vol 13 (2) ◽  
pp. 31
Author(s):  
Cristòfol Rovira ◽  
Lluís Codina ◽  
Carlos Lopezosa
Keyword(s):  
Search Engine ◽  
Search Engines ◽  
Google Scholar ◽  
Ranking Algorithm ◽  
Relevance Ranking ◽  
Language Bias ◽  
Engineering Research ◽  
Search Engine Optimization ◽  
Conference Papers ◽  
Academic Search

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.

scholarly journals Otimização da produção acadêmico-científica para mecanismos de busca acadêmicos (ASEO)

BIBLOS ◽  
2019 ◽  
Vol 33 (2) ◽  
pp. 4-19
Author(s):  
Emanuelle Torino ◽  
Gustavo Lunardelli Trevisan ◽  
Silvana Aparecida Borsetti Gregorio Vidotti
Keyword(s):  
Search Engine ◽  
Search Engine Optimization ◽  
Academic Search

A crescente disponibilização de websites e conteúdos informacionais na web torna necessária a adoção de estratégias e técnicas para que os mecanismos de busca possam rastrear e coletar informações contidas, por exemplo, no código fonte, na URL e nos links das páginas e para que tenham melhor ranqueamento na página de resultados. Tais estratégias e técnicas podem ser adotadas no contexto da otimização de conteúdos para mecanismo de busca, Search Engine Optimization. De igual maneira, há conteúdos oriundos da produção acadêmico-científica disponibilizados em ambientes informacionais digitais web, que podem ser recuperados por mecanismos de busca acadêmicos e, cujo ranqueamento na página de resultados pode ocorrer com a utilização de estratégias e técnicas de Academic Search Engine Optimization. Assim, na perspectiva ampliação da visibilidade e do uso da produção acadêmico-científica este estudo objetiva apresentar estratégias e técnicas de otimização de conteúdos para mecanismos de busca acadêmicos, sob a ótica do autor. Para tanto, utiliza-se da pesquisa bibliográfica para discorrer acerca da temática e apresentar os discussões, com base na Ciência da Informação. Como resultados, tem-se um modelo conceitual de otimização da produção acadêmico-científica para mecanismos de busca acadêmicos, sob a perspectiva do autor, no qual são apresentadas as estratégias e técnicas de otimização de conteúdos para mecanismo de busca acadêmico que podem ser utilizados para a ampliação da visibilidade, do uso e da possibilidade de citação.

scholarly journals Repositorios y redes sociales académicas, dos vehículos para un mismo conocimiento científico

revista PH ◽  
2020 ◽  
Author(s):  
Alexandre López-Borrull
Keyword(s):  
Search Engine ◽  
Redes Sociales ◽  
Search Engine Optimization ◽  
Academic Search

Se presenta una contribución al debate sobre los repositorios y las redes sociales académicas. Según la visión del autor, son herramientas complementarias en un mundo marcado por la presión por publicar y conseguir más impacto. Se presentan algunas de las cuestiones a considerar en el debate, como la privacidad, los modelos de negocio, la ciencia abierta y el academic search engine optimization (ASEO).

scholarly journals Content Management Systems Performance and Compliance Assessment Based on a Data-Driven Search Engine Optimization Methodology

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 259
Author(s):  
Ioannis Drivas ◽  
Dimitrios Kouis ◽  
Daphne Kyriaki-Manessi ◽  
Georgios Giannakopoulos
Keyword(s):  
Search Engine ◽  
Common Knowledge ◽  
Organizational Structures ◽  
Content Management ◽  
Data Driven ◽  
Management Systems ◽  
Search Engine Optimization ◽  
Optimization Methodology ◽  
Content Management Systems ◽  
The Web

While digitalization of cultural organizations is in full swing and growth, it is common knowledge that websites can be used as a beacon to expand the awareness and consideration of their services on the Web. Nevertheless, recent research results indicate the managerial difficulties in deploying strategies for expanding the discoverability, visibility, and accessibility of these websites. In this paper, a three-stage data-driven Search Engine Optimization schema is proposed to assess the performance of Libraries, Archives, and Museums websites (LAMs), thus helping administrators expand their discoverability, visibility, and accessibility within the Web realm. To do so, the authors examine the performance of 341 related websites from all over the world based on three different factors, Content Curation, Speed, and Security. In the first stage, a statistically reliable and consistent assessment schema for evaluating the SEO performance of LAMs websites through the integration of more than 30 variables is presented. Subsequently, the second stage involves a descriptive data summarization for initial performance estimations of the examined websites in each factor is taking place. In the third stage, predictive regression models are developed to understand and compare the SEO performance of three different Content Management Systems, namely the Drupal, WordPress, and custom approaches, that LAMs websites have adopted. The results of this study constitute a solid stepping-stone both for practitioners and researchers to adopt and improve such methods that focus on end-users and boost organizational structures and culture that relied on data-driven approaches for expanding the visibility of LAMs services.

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