Academic Search Engine Optimization (ASEO): Optimizing Scholarly Literature for Google Scholar & Co.

2010 ◽  
Vol 41 (2) ◽  
pp. 176-190 ◽  
Author(s):  
Jöran Beel ◽  
Bela Gipp ◽  
Erik Eilde
Keyword(s):  
Search Engine ◽  
Google Scholar ◽  
Small Class ◽  
Scholarly Literature ◽  
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.

Google Scholar's Filter Bubble

2017 ◽  
pp. 211-229
Author(s):  
Ke Yu ◽  
Nazeem Mustapha ◽  
Nadeem Oozeer
Keyword(s):  
Social Science ◽  
Search Engine ◽  
Google Scholar ◽  
Online Search ◽  
Search Behaviour ◽  
Search Results ◽  
Search Terms ◽  
Exploratory Experiment ◽  
Filter Bubble ◽  
Academic Search

This chapter investigates the allegation that popular online search engine Google applies algorithms to personalise search results therefore yielding different results for the exact same search terms. It specifically examines whether the same alleged filter bubble applies to Google's academic product: Google Scholar. It reports the results from an exploratory experiment of nine keywords carried out for this purpose, varying variables such as disciplines (Natural Science, Social Science and Humanities), geographic locations (north/south), and levels (senior/junior researchers). It also reports a short survey on academic search behaviour. The finding suggests that while Google Scholar, together with Google, has emerged as THE dominant search engine among the participants of this study, the alleged filter bubble is only mildly observable. The Jaccard similarity of search results for all nine keywords is strikingly high, with only one keyword that exhibits a localized bubble at 95% level. This chapter therefore concludes that the filter bubble phenomenon does not warrant concern.

scholarly journals SEforRA: A Bibliometrics-ready Academic Digital Library Search Engine Alternative

2020 ◽  
Author(s):  
Muhaemin Sidiq ◽  
Ivan Hanafi ◽  
Fajar J. Ekaputra
Keyword(s):  
Search Engine ◽  
Digital Libraries ◽  
Incomplete Data ◽  
Complete Information ◽  
Google Scholar ◽  
Research Articles ◽  
Scientific Publications ◽  
Text Data ◽  
Academic Publications ◽  
Academic Search

Naturally, not all researchers can develop their own software to search for academic publications from digital libraries. Nevertheless, at several stages of their research, they will need to search digital libraries for relevant scientific publications and bibliometric information. There are typically two approaches used by researchers to search for scientific publications: (i) using Google Scholar search, or (ii) using publication metadata available from several sources, such as CrossRef and publishers. However, in developing countries like Indonesia, neither option provided users with complete information, since (i) Google Scholar does not provide bibliometric details, and (ii) complete bibliometric information from other sources is often not available due to incomplete data (e.g., CrossRef) or the necessity to pay a subscription fee (e.g., Springer and Elsevier). The development of Search Engine for Research Articles (SEforRA) is a solution to this issue which provides researchers with bibliometricready publication metadata. SEforRA extracts and processes data from CrossRef, publishers, and other sources to provide an integrated platform for researchers to search and retrieve publication metadata, which is ready to use further in their research. Keywords: search engine for research articles, academic search engines, text data mining, bibliometrics

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 Indeksasi Jurnal di Lingkungan Universitas Lancang Kuning

2020 ◽  
Vol 6 (1) ◽  
pp. 37-46
Author(s):  
Nining Sudiar ◽  
Hadira Latiar
Keyword(s):  
Search Engine ◽  
Science And Technology ◽  
Google Scholar ◽  
Academic Search

Penelitian ini bertujuan untuk menganalisis perkembangan indeksasi jurnal yang ada di Universitas Lancang Kuning dengan cara menghitung jenis lembaga pengindeks dan  menghitung jumlah indeksasi yang pada setiap jurnal. Metode penelitian yang digunakan adalah deskriptif kuantitatif dengan pendekatan kepustakaan melalui penelusuran pada URL jurnal Unilak. Hasil penelitian menunjukan bahwa ada 38 jenis pengindeks dengan jumlah 143 lembaga pengindeks yang terdapat pada 16 jurnal.  Lima peringkat lembaga pengindeks terbanyak, pertama, Crossref dan Google Scholar yaitu 16 jurnal atau 18,83%, kedua Garuda sebanyak 11 jurnal atau 7,69%, ketiga OCLC WorldCat sebanyak 10 jurnal atau 6,99 %, keempat ada pada Bielefels Academic Search Engine (BASE) yaitu 9 jurnal atau 6,29 % dan yang kelima ada pada Science and Technology Index (Sinta) dengan jumlah 8 jurnal atau 5, 59%. Dapat disimpulkan bahwa lembaga pengindeks yang paling banyak dimiliki jurnal Unilak adalah lembaga pengindeks bereputasi rendah yaitu 95,11 %, lembaga pengindeks bereputasi sedang 4,89% dan belum memiliki lembaga pengindeks bereputasi tinggi seperti Scopus dan Thomson.

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 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).

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