Google Scholar's ranking algorithm: The impact of citation counts (An empirical study)

@article{Beel2009GoogleSR,
  title={Google Scholar's ranking algorithm: The impact of citation counts (An empirical study)},
  author={Joeran Beel and Bela Gipp},
  journal={2009 Third International Conference on Research Challenges in Information Science},
  year={2009},
  pages={439-446}
}
  • J. Beel, Bela Gipp
  • Published 22 April 2009
  • Computer Science
  • 2009 Third International Conference on Research Challenges in Information Science
Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. In a recent study we partly reverse-engineered the algorithm. This paper presents the results of our second study. While the previous study provided a broad overview, the current study focused on analyzing the correlation of an article's citation count and its ranking in Google Scholar. For this study, citation counts and rankings of 1,364,757 articles were analyzed. Some… 
Google Scholar's Ranking Algorithm: The Impact of Articles' Age (An Empirical Study)
  • J. Beel, Bela Gipp
  • Education
    2009 Sixth International Conference on Information Technology: New Generations
  • 2009
TLDR
The analysis revealed that an article�'s age seems to play no significant role in Google Scholar’s ranking algorithm, and it was discussed why this might lead to a suboptimal ranking.
Google Scholar ’ s Ranking Algorithm : The Impact of Articles ’ Age ( An Empirical Study )
Google Scholar is one of the major academic search engines but its ranking algorithm for academic articles is unknown. In recent studies we partly reverse-engineered the algorithm. This paper
Google Scholar’s Ranking Algorithm : An Introductory Overview
TLDR
The first steps to reverse-engineering Google Scholar’s ranking algorithm are performed and the results may help authors to optimize their articles for Google Scholar and enable researchers to estimate the usefulness of Google Scholar with respect to their search intention and hence the need to use further academic search engines or databases.
’ s Ranking Algorithm : An Introductory Overview Joeran
TLDR
The first steps to reverse-engineering Google Scholar’s ranking algorithm are performed and the results may help authors to optimize their articles for Google Scholar and enable researchers to estimate the usefulness of Google Scholar with respect to their search intention and hence the need to use further academic search engines or databases.
Ranking by Relevance and Citation Counts, a Comparative Study: Google Scholar, Microsoft Academic, WoS and Scopus
TLDR
This study analyzes and compares the relevance ranking algorithms employed by various academic platforms to identify the importance of citations received in their algorithms, and indicates that citation counts are clearly the main SEO factor in these academic search engines.
Language Bias in the Google Scholar Ranking Algorithm
TLDR
A reverse engineering research methodology based on a statistical analysis that uses Spearman's correlation coefficient points 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.
A Novel Improvement to Google Scholar Ranking Algorithms Through Broad Topic Search
TLDR
The expectation of this design is to introduce modern algorithm techniques to academic search engines, resulting in greater quality, discoverability, and core topic diversity of published research.
Academic Search Engine Spam and Google Scholar's Resilience Against it
TLDR
The results show that academic search engine spam is indeed— and with little effort—possible, and whether academicsearch engine spam could become a serious threat to Web-based academic search engines is discussed.
A novel improvement to google scholar algorithms through broad topic search
TLDR
The expectation of this design is to introduce modern algorithm techniques to academic search engines, resulting in greater quality, discoverability, and core topic diversity of published research.
Scientific Research Paper Ranking Algorithm PTRA: A Tradeoff between Time and Citation Network
TLDR
Citation index has the highest impact on Google scholars ranking algorithm results, compared with PTRA, which depends on the citation index and publication venue to rank the results but with less impact than the paper age.
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References

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Google Scholar's Ranking Algorithm: The Impact of Articles' Age (An Empirical Study)
  • J. Beel, Bela Gipp
  • Education
    2009 Sixth International Conference on Information Technology: New Generations
  • 2009
TLDR
The analysis revealed that an article�'s age seems to play no significant role in Google Scholar’s ranking algorithm, and it was discussed why this might lead to a suboptimal ranking.
Google Scholar’s Ranking Algorithm : An Introductory Overview
TLDR
The first steps to reverse-engineering Google Scholar’s ranking algorithm are performed and the results may help authors to optimize their articles for Google Scholar and enable researchers to estimate the usefulness of Google Scholar with respect to their search intention and hence the need to use further academic search engines or databases.
Citation Analysis: A Comparison of Google Scholar, Scopus, and Web of Science
TLDR
A case study comparing citations found in Scopus and Google Scholar with those found in Web of Science for items published by two Library and Information Science full-time faculty members and a brief overview of a prototype system called CiteSearch, which analyzes combined data from multiple citation databases to produce citation-based quality evaluation measures.
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