Google Scholar's Ranking Algorithm: The Impact of Articles' Age (An Empirical Study)

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Google Scholar’s Ranking Algorithm: The Impact of
Articles’ Age (An Empirical Study)


Jöran Beel & Bela Gipp
Otto-von-Guericke University
Department of Computer Science
ITI / VLBA-Lab / Scienstein
Magdeburg, Germany
j.beel|b.gipp@scienstein.org


Abstract
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 presents
the results of our third study. While the first study
provided a broad overview and the second study
focused on researching the impact of citation counts,
the current study focused on analyzing the correlation
of an article’s age and its ranking in Google Scholar.
In other words, it was analyzed if older/recent
published articles are more/less likely to appear in a
top position in Google Scholar’s result lists. For our
study, age and rankings of 1,099,749 articles
retrieved via 2,100 search queries were analyzed. The
analysis revealed that an article’s age seems to play
no significant role in Google Scholar’s ranking
algorithm. It is also discussed why this might lead to a
suboptimal ranking.
1. Introduction
With increasing use of academic search engines it
becomes increasingly important for scientific authors
that their research articles are well ranked in those
search engines in order to reach their audience. To
optimize research papers for academic search engines,
such as Google Scholar or Scienstein.org, knowledge
about ranking algorithms is essential. For instance, if
search engines consider how often a search term
occurs in an article‟s full text, authors should use the
most relevant keywords in their articles whenever
possible to achieve a top ranking.
For users of academic search engines, knowledge
about applied ranking algorithms is also essential for
two reasons. Firstly, users should know about the

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algorithms in order to estimate the search engine‟s
robustness to manipulation attempts by authors and
spammers and therefore the trustworthiness of the
results. Secondly, knowledge of ranking algorithms
enables researchers to estimate the usefulness of
results in respect to their search intention. For
instance, researchers interested in the latest trends
should use a search engine putting high weight on the
publications‟ date. Users searching for standard
literature should choose a search engine putting high
weight on citation counts. In contrast, if a user
searches for articles from authors advancing a view
different from the majority, search engines putting
high weight on citation counts might not be
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Therefore, this paper deals with the question of
how Google Scholar ranks its results. The paper is
structured as follows. In the second section related
work about Google Scholar‟s ranking algorithm is
presented. The third section covers the research
objectives while the fourth section explains the
utilized methodology. Finally, the results and their
interpretation follow.
2. Related Work
Due to different user needs, many academic
databases and search engines enable the user to choose
a ranking algorithm. For instance, ScienceDirect lets
users select between date and relevance1, IEEE Xplore
offers in addition a ranking by title and ACM Digital
Library allows users to choose whether to sort results
by relevance, publication date, alphabetically by title
or journal, citation counts or downloads. However,
these „algorithms‟ can be considered trivial since users
can select only one ranking criteria and are not
allowed to use a (weighed) combination of them.

1 „Relevance‟ in most cases means that the more often a
search term occurs in a document, the more relevant it is
considered.
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Position in Google Scholar

Figure 1: Mean Citation Count
Google Scholar is one of the few academic search
engines combining several approaches in a single
algorithm2. Several studies about Google Scholar
exist. For instance, about data overlap with other
academic search engines such as Scopus and Web of
Science [1], [2], Google Scholar‟s coverage of the
literature in general and in certain research fields
[3], [4], the suitability to use Google Scholar‟s citation
counts for calculating bibliometric indices such as the
h-index [5] and the reliability of Google Scholar as a
serious information source in general [6], [7]. Google
Scholar itself publishes only vague information about
its ranking algorithm: Google Scholar sorts “articles
the way researchers do, weighing the full text of each
article, the author, the publication in which the article
appears, and how often the piece has been cited in
other scholarly literature” [8]. Any other details or
further explanation is not available.
Although Google Scholar‟s ranking algorithm has
a significant influence on which academic articles are
read by the scientific community, we could not find
any studies about Google Scholar‟s ranking algorithm
despite our own ones [9], [10]. From our previous
studies we know that
 Google Scholar‟s ranking algorithm puts
high weight on words in the title.
 Google Scholar considers only those words
that are directly included in an article and
does not consider synonyms of those words.
 Google Scholar seems to put no or low weight
on the frequency with which search terms
occur in the full text. That means an article

2 Others are, for instance, CiteSeer and Scienstein.org [11,
12]

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will not be ranked higher for a certain search
just because the search term occurs frequently
in the full text.
 Google Scholar is not indexing text
embedded via pictures.
 Google Scholar uses different ranking
algorithms for a keyword search in the full
text, keyword search in the title, the „related
articles‟ function and the „cited by‟ function.
 Google Scholar‟s ranking algorithm puts
high weight on author and journal names.
 Google Scholar‟s ranking algorithm weighs
heavily on articles‟ citation counts (see
Figure 1), whereas different patterns were
discovered.
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Mean

Figure 2: Mean Publication Year per Position
Since citation counts have a strong impact on
Google Scholar‟s rankings, one could assume that
older articles are found more often in top positions,
since older publications naturally have had more time
to be cited. As a consequence, this practice would
strengthen the Matthew Effect3. To counteract the
Matthew Effect and since one might assume that most
researchers have an interest in the most recent
research results rather than old ones, it seems
plausible to rank recent articles better than older ones.
Our previous research indicated that publications
from all years are approximately evenly distributed
throughout Google Scholars‟ result list (see Figure 2).

3 The Matthew Effect describes that well known authors are
more often cited just because they are well known [13].
Related to search engines this means: Articles with many
citations will be more likely displayed in top positions,
therefore get more readers and receive more citations,
which then consolidate their lead over lesser cited articles.
Therefore we concluded that an article‟s age plays a
significant role in Google Scholar‟s ranking
algorithm. However, the sample size was small, so
further research was needed to confirm or reject this
first conclusion.
3. Research Objective
The research objective of the current study was to
analyze whether Google Scholar considers articles‟
age in its ranking algorithm and if so to what extent.
Since Google Scholar offers two search modes
(search in title and search in full text) and our
previous study indicated that both search modes apply
different ranking algorithms we also researched
whether Google Scholar‟s different ranking
algorithms weigh differently on an articles‟ age.
4. Methodology
Google Scholar displays for most articles their
publication year in the result list. To obtain
publication years for a significant number of papers,
we developed a Java program to parse Google Scholar.
This program sends search queries to Google Scholar
and stores publication years and positions of all
returned results in a .csv file. Due to Google Scholar‟s
limitations, only a maximum of 1,000 results per
search query was retrievable. The parsing process was
performed twice, each time with 1,050 search queries
whereas the 1,050 search queries consisted of 350
single-word search queries, 350 double-word search
queries and 350 triple-word search queries4. In the
first run, search terms were searched in the full text.
In the second run, search terms were searched in the
title.
Table 1: Amount of Search Results by Number of Search
Terms (Full Text Search)
[0,1] [2, 10] [11, 50] [51, 250] [251, 1000] [1001, 10000] [10001, *] Total
Absolute 0 0 0 0 0 2 348 350
Relative 0,0% 0,0% 0,0% 0,0% 0,0% 0,6% 99,4% 100%
Absolute 0 0 0 0 3 24 323 350
Relative 0,0% 0,0% 0,0% 0,0% 0,9% 6,9% 92,3% 100%
Absolute 0 0 0 1 4 86 259 350
Relative 0,0% 0,0% 0,0% 0,3% 1,1% 24,6% 74,0% 100%
Absolute 0 0 0 1 7 112 930 1050
Relative 0,0% 0,0% 0,0% 0,1% 0,7% 10,7% 88,6% 100%
Number of Search Results
Total
Single
Terms
Double
Term
Triple
Term

From 1,050 full text searches, all search queries
returned two or more results (see Table 1) and could
be used for the analysis. From 1,050 title searches, 511
returned either a zero or one result and were not
considered for further analysis (see Table 2). This was

4 The words for creating the search queries were extracted
from an academic word list [14]

caused by the way search queries were created. They
were created automatically by combining different
words from a word list which resulted in some
senseless search queries such as „finish father‟ or
„excessive royalty‟. While sufficient documentation
exists in which, for instance, the words „finish‟ and
„father‟ occur somewhere in the full text, no
documents exist which include these words in the title.
Table 2: Amount of Search Results by Number of Search
Terms (Title Search)
[0,1] [2, 10] [11, 50] [51, 250] [251, 1000] [1001, 10000] [10001, *] Total
Absolute 0 1 1 12 23 102 211 350
Relative 0,0% 0,3% 0,3% 3,4% 6,6% 29,1% 60,3% 100%
Absolute 166 89 54 27 11 3 0 350
Relative 47,4% 25,4% 15,4% 7,7% 3,1% 0,9% 0,0% 100%
Absolute 345 5 0 0 0 0 0 350
Relative 98,6% 1,4% 0,0% 0,0% 0,0% 0,0% 0,0% 100%
Absolute 511 95 55 39 34 105 211 1050
Relative 48,7% 9,0% 5,2% 3,7% 3,2% 10,0% 20,1% 100%
Number of Search Results
Si gle
Terms
Double
Term
Triple
Term
Total

Overall, data from 1,561 search queries (1,050
searches in the full text and 511 searches in the title)
was used for further analysis. The 1,561 search
queries returned a total of 1,364,757 results
(1,032,766 articles for full text searches and 331,991
articles for title searches). For 810,793 of the
1,032,766 articles retrieved via full-text search and
288,956 of the 331,991 articles retrieved via title
search, Google Scholar displayed the publication year.
Those years and the articles' rankings were stored and
analyzed. To verify correct execution of the Google
Scholar parser, spot checks were performed.
All results of the search queries were visualized as
graphs to recognize patterns. In addition, the mean,
median, and modal of each position was calculated
and displayed in a graph. Overall, a total of 1,567
graphs were created and inspected individually.
5. Results
On first glance, results of the current study seem to
confirm our previous results. Graphs of individual
search queries show no significant interdependency
between an article‟s age and its ranking in Google
Scholar (see also [10]). This is true for all kind of
search queries such as searches in full-text or title and
searches with single-word, double-word and triple-
word queries. The graphs show that publications from
all years are evenly distributed throughout the result
list (see Figure 3, Figure 4 and Figure 5)5.

5 The graphs also show that Google Scholar has far more
documents from the 90s and current decade in its database
than from decades before. However, this is out of the
current study‟s scope.
However, looking at the average age, another
impression evolves. Figure 6 displays the average
publication year (mean) for each position in Google
Scholar. It shows clearly that in the top positions
articles are on average older than articles in the
remaining positions6.
A look at the numbers confirms this assumption.
While those papers ranked in position 1 by Google
Scholar were on average published in 1992, papers on
position 5 were on average published in 1993, papers
on position 100 in 1994 and papers on position 500 in
1995 (see Table 3). Graphs for title-searches look
similar (see Figure 7) and no significant differences
occurred between single-word, double word and triple
word search queries7.
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2008
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Figure 3: Search Query 'Future'
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Figure 4: Search Query 'Google Scholar'


6 Graphs for the modal and median publication year show
similar pictures.
7 In all graphs, some outliers can be observed in the very last
positions. This is due to Google Scholar which often does
not return the very last results. Therefore the means for
the last positions was based on few sample data and hence
some outliers could spoil the results.

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Figure 5: Search Query 'Climate Change Discussion'

1989
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Figure 6: Mean Publication Year (Full-Text Search)

Table 3: Mean Publication Year (Selected Positions) Posi ion
Publication
Year (Mean) Position
Publication
Year (Mean)
1 1992 10 1994
2 1992 50 1994
3 1992 100 1994
4 1993 250 1994
5 1993 500 1995


1989
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Figure 7: Mean Publication Year (Title Search)
6. Interpretation and Discussion
Taking into consideration that Google Scholar
might become as popular for academic articles as
Google is for web pages, we hope to stimulate a
discussion with our research into how ranking
algorithms of academic search engines should be
designed. We believe that users should be able to
adjust ranking algorithms to their individual search
intension (search for standard literature, search for
latest research trends, search for articles by authors
advancing a view different from the mainstream, etc.).
If a search engine does not offer this option, as is
the case with Google Scholar, users should at least
have basic knowledge about the applied ranking
algorithm. Only this way they can assess the suitability
of an academic search engine for their search
intension.
Our research shows that in Google Scholar older
articles are found more often in top positions than
recent articles. This is probably due to Google
Scholar‟s strong focus on citation counts and due to
Google Scholar putting no or low weight on an
article‟s publication date. As a consequence, Google
Scholar is rather suitable for finding standard
literature than the latest research results.
7. Further Research & Data Sharing
This is our third paper about Google Scholar‟s
ranking algorithm and the algorithm is still far from
being known. We invite researchers to join us and
would be happy to share our Google Scholar parser
and gathered data. Please send us an email if you are
interested in the data or software.
8. Acknowledgements
Our thanks go to Ammar Shaker for supporting the
development of the Google Scholar parser.
9. References
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