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


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Academic Search Engine Optimization (ASEO): Optimizing
Scholarly Literature for Google Scholar & Co.
Joeran Beel
UC Berkeley
School of Information
Bela Gipp
UC Berkeley
School of Information
Erik Wilde
UC Berkeley
School of Information
This article introduces and discusses the concept of academic
search engine optimization (ASEO). Based on three recently
conducted studies, guidelines are provided on how to optimize
scholarly literature for academic search engines in general and
for Google Scholar in particular. In addition, we briefly discuss
the risk of researchers’ illegitimately ‘over-optimizing’ their
academic search engines, academic search engine optimization,
ASEO, Google Scholar, ranking algorithm, search engine
optimization, SEO
Researchers should have an interest in ensuring that their articles
are indexed by academic search engines
such as Google Scholar,
IEEE Xplore, PubMed, and, which greatly improves
their ability to make their articles available to the academic
community. Not only should authors take an interest in seeing
that their articles are indexed, they also should be interesting in
where the articles are displayed in the results list. Like any other
type of ranked search results, articles displayed in top positions
are more likely to be read.
This article presents the concept of academic search engine
optimization (ASEO) to optimize scholarly literature for
academic search engines. The first part of the article covers
related work that has been done mostly in the field of general
search engine optimization for Web pages. The second part
defines ASEO and compares it to search engine optimization for
Web pages. The third part provides an overview of ranking
algorithms of academic search engines in general, followed by an
overview of Google Scholar’s ranking algorithm. Finally,
guidelines are provided on how authors can optimize their
articles for academic search engines. This article does not cover
how publishers or providers of academic repositories can
optimize their Web sites and repositories for academic search
engines. The guidelines are based on three studies we have
recently conducted [1-3] and on our experience in developing the
academic search engine
In this article we do not distinguish between ‘academic
databases’ and ‘academic search engines’; the latter term is
used as synonym for both.
On the Web, search engine optimization (SEO) for Web sites is a
common procedure. SEO involves creating or modifying a Web
site in a way that makes it ‘easier for search engines to both
crawl and index [its] content [4]. There exists a huge community
that discusses the latest trends in SEO and provides advice for
Webmasters in forums, blogs, and newsgroups.
Even research
articles and books exist on the subject of SEO [5-10]. When SEO
began, many expressed their concerns that it would promote
spam and tweaking, and, indeed, search-engine spam is a serious
issue [11-26]. Today, however, SEO is a common and widely
accepted procedure and overall, search engines manage to
identify spam quite well. Probably the strongest argument for
SEO is the fact that search engines themselves publish guidelines
on how to optimize Web sites for search engines [4, 27]. But
similar information on optimizing scholarly literature for
academic search engines does not exist, to our knowledge.
2.1 Introduction to Academic Search Engine
Optimization (ASEO)
Based on the definition of search engine optimization for Web
pages (SEO), we define academic search engine optimization
(ASEO) as follows:
Academic search engine optimization (ASEO) is the creation,
publication, and modification of scholarly literature in a
way that makes it easier for academic search engines to both
crawl it and index it.
ASEO differs from SEO in four significant respects. First, for
Web search, Google is the market leader in most (Western)
countries [28]. This means that for Webmasters (focusing on
Western Internet users), it is generally sufficient to optimize their
Web sites for Google. In contrast, no such market leader exists
for searching academic articles, and researchers would need to
Google Scholar offers some information for publishers on how
to get their articles indexed by Google Scholar and ranked well
[35]. However, this information is superficial in comparison to
other SEO articles, and the information is not aimed at authors.
Preprint of: Joeran Beel, Bela Gipp, and Erik Wilde. Academic Search Engine Optimization (ASEO): Optimizing Scholarly Literature for Google Scholar and
Co. Journal of Scholarly Publishing, 41 (2): 176190, January 2010. doi: 10.3138/jsp.41.2.176. University of Toronto Press. Downloaded from
Visit for more of our papers about Google Scholar, Academic Search Engine Spam, and Academic Search Engine Optimization
optimize their articles for several academic search engines. If
these search engines are based on different crawling and ranking
methods, optimization can become complicated.
Second, Webmasters usually do not need to worry about whether
their site is indexed by a search engine: as long as any Web page
is linked to an already indexed page, it will be crawled and
indexed by Web search engines at some point. The situation is
different in academia, where only a fraction of all published
material is available on the Web and accessible to Web-based
academic search engines such as CiteSeer. Most academic
articles are stored in publishers’ databases; they are part of the
‘academic invisible web,’ [29] and (academic) search engines
usually cannot access and index these articles. A few academic
search engines, such as Scirus and Google Scholar, cooperate
with publishers, but still they do not cover all existing articles
[30-32]. Researchers therefore need to think seriously about how
to get their articles indexed by academic search engines.
Third, Webmasters can alter their pages by adding or replacing
words and links, deleting pages, offering multiple versions with
slight variations, and so on; in this way they can test new
methods and adapt to changes in ranking algorithms. Scholarly
authors can hardly do so: once an article is published, it is
difficult and sometimes impossible to alter it. Therefore, ASEO
needs to be performed particularly carefully.
Finally, Web search engines usually index all text on a Web site,
or at least the majority of it. In contrast, some academic search
engines do not index a document’s full text but instead index
only the title and abstract. This means that for some academic
search engines authors need to focus on the article’s title and
abstract, but in other cases they still have to consider the full text
for other search engines.
2.2 An Overview of Academic Search
Engines’ Ranking Algorithms
The basic concept of keyword-based searching is the same for all
major (academic) search engines. Users search for a search term
in a certain document field (e.g., title, abstract, body text), or in
all fields, and all documents containing the search term are listed
on the results page. Academic search engines use different
ranking algorithms to determine in which position the results are
displayed. Some let the user choose one factor on which to rank
the results (common ranking factors are publication date, citation
count, author or journal name and reputation, and relevance of
the document); others combine the ranking factors into one
algorithm, and, more often than not, the user has no influence on
the factor’s weighting.
The relevance of a document is basically a function of how often
the search term occurs in that document and in which part of the
document it occurs. Generally speaking, the more often a search
term occurs in the document, and the more important the
document field is in which the term occurs, the more relevant the
document is considered
. This means that an occurrence in the
title is weighted more heavily than an occurrence in the abstract,
Some algorithms, such as the BM25(f ), saturate when a word
occurs often in the text [36].
which carries more weight than an occurrence in a (sub)heading,
than in the body text, and so on. Possible document fields that
may be weighted differently by academic search engines are:
Author names
Author keywords
Body text
Tables and figures
Publication name (name of journal, conference,
proceedings, book, etc.)
User keywords (Social tags)
Social annotations
The metadata of electronic files are especially important for
academic search engines crawling the Web. When a search
engine finds a PDF on the Web, it does not know whether this
PDF represents an academic article, or which one it belongs to;
therefore, the PDF must be identified, and one way to do this is
by extracting the author and title. This can be done by analyzing
the full text of the document or the metadata of the PDF.
It is also important to note that text in figures and tables usually
is indexed only if it is embedded as real text or within a vector
graphic. If text is embedded as a raster graphic (e.g., *.bmp,
*.png, *.gif, *.tif, *.jpg), most, if not all, search engines will not
index the text (see Figures 1 and 2 for an illustration of
differences between vector and raster/bitmap graphics).
To our
knowledge, none of the major academic search engines currently
considers synonyms. This means that a document containing only
the term ‘academic search engine’ would not be found via a
search for ‘scientific paper search engine’ or ‘academic
database.’ What most academic search engines do is stemming:
words are reduced to their stems (e.g., ‘analysed’ and ‘analysing’
would be reduced to ‘analyse’).
2.3 Google Scholar’s Ranking Algorithm
Google Scholar is one of those search engines that combine
several factors into one ranking algorithm. The most important
factors are relevance, citation count, author name(s), and name of
Some of the data could be retrieved from the document full
text, other from the metadata (of electronic files)
Theoretically search engines could index the text in
raster/bitmap graphics, but they would have to apply optical
character recognition (OCR). To our knowledge, no search
engine currently does this, although some are using OCR to
index complete scans of scholarly literature.
Google Scholar offers different search functions. For instance, it
is possible to search for ‘related articles’ and recent articles.’
In this article we focus on the normal ranking algorithm, which
is applied for the standard keyword search.
2.3.1 Relevance
Google Scholar focuses strongly on document titles. Documents
containing the search term in the title are likely to be positioned
near the top of the results list. Google Scholar also seems to
consider the length of a title: In a search for the term ‘SEO,’ a
document titled ‘SEO: An Overview’ would be ranked higher
than one titled ‘Search Engine Optimization (SEO): A Literature
Survey of the Current State of the Art.’
Although Google Scholar indexes entire documents, the total
search term count in the document has little or no impact. In a
search for ‘recommender systems,’ a document containing fifty
instances of this term would not necessarily be ranked higher
than a document containing only ten instances.
This figure (including all text and
shapes) should scale and should
be still well readable.
You could also search for the
term "aseoVectorExample" in
Google Scholar, Google or other
(academic) search engines and
you will find this document
You should also be
able to mark this text
and copy it, for
instance, to your
word processing
All this is possible because this figure is a vector graphic
If you are currently
reading this document in
electronic form (e.g.
PDF), enlarge it and see
what happens…
well readable.
Figure 1: Example of a Vector Graphic
Like other search engines, Google Scholar does not index text in
figures and tables inserted as raster/bitmap graphics, but it does
index text in vector graphics. It is also known that neither
synonyms nor PDF metadata are considered.
2.3.2 Citation Counts
Citation counts play a major role in Google Scholar’s ranking
algorithm, as illustrated in Figure 3, which shows the mean
citation count for each position in Google Scholar.
It is clear
that, on average, articles in the top positions have significantly
more citations than articles in the lowest positions. This means
that to achieve a good ranking in Google Scholar, many citations
are essential. Google Scholar seems not to differentiate between
self-citations and citations by third parties.
On average, articles at position 1 had 834 citations, articles at
position 2 had 552, articles at position 3 had 426, and articles
at position 1000 had fifty-three. The study was based on
1,032,766 results produced by 1050 search queries in
November 2008. For more detail see [1].
Figure 2: Example of a Bitmap Graphic
2.3.3 Author and Publication Name
If the search query includes an author or publication name, a
document in which either appears is likely to be ranked high. For
instance, seventy-four of the top 100 results of a search for
‘arteriosclerosis and thrombosis cure’ were articles about various
(medical) topics from the journal Arteriosclerosis, Thrombosis,
and Vascular Biology, many of which did not include the search
term either in the title or in the full text [2].
0250 500 750 1000
Citation Counts
Position in Google Scholar
Figure 3: Mean Citation Count per Position8
2.3.4 Other factors
Google Scholar’s standard search does not consider publication
dates. However, Google Scholar offers a special search function
for ‘recent articles,’ which limits results to articles published
within the past five years. Furthermore, Google Scholar claims to
consider both publication and author reputation [33]. However,
we could not research the influence of these factors because of a
lack of data, and therefore we do not consider them here.
2.3.5 Sources Indexed by Google Scholar
Bert van Heerde, a professional in the field of SEO, uses the
term ‘invitation based search engine’ to describe Google Scholar:
Only articles from trusted sources and articles that are ‘invited’
(cited) by articles already indexed are included in the database
[34]. ‘Trusted sources,’ in this case, are publishers that cooperate
directly with Google Scholar, as well as publishers and
Webmasters who have requested that Google Scholar crawl their
databases and Web sites.
Once an article is included in Google Scholar’s database, Google
Scholar searches the Web for corresponding PDF files, even if a
trusted publisher has already provided the full text.
It makes no
difference on which site the PDF is published; for instance,
Google Scholar has indexed PDF files of our articles from the
publisher’s site, our university’s site, our private home pages,
and PDFs found on the Web are linked directly on
Google Scholar’s results pages, in addition to the link to the
publisher’s full text (see Figure 4 for an illustrative example).
Figure 4: Linking database entries with external PDFs
If different PDF files of an article exist, Google Scholar groups
them to improve the article’s ranking [35]. For instance, if a
preprint version of an article is available on the author’s Web
page and the final version is available on the publisher’s site,
Google indexes both as one version. If the two versions contain
different words, Google Scholar associates all contained words
with the article. This is an interesting feature that we will
discuss in more detail in the next section.
3.1 Preparation
In the beginning it is necessary to think about the most important
words that are relevant to the article. It is not possible to
optimize one document for dozens of keywords, so it is better to
choose a few. There are tools that help in selecting the right
keywords, such as Google Trends, Google Insights, Google
Adwords keyword tool, Google Searchbased keyword tool, and
Visit to
ask Google Scholar to crawl your Web site containing scholarly
Google Scholar also indexes other file types, such as
PostScript (*.ps), Microsoft Word (*.doc), and MS PowerPoint
(*.ppt). Here we focus on PDF, which is the most common
format for scientific articles.
Google Trends
Google Insights
It might be wise not to select those keywords that are most
popular. It is usually a good idea to query the common academic
search engines using each proposed keyword; if the search
already returns hundreds of documents, it may be better to
choose another keyword with less competition.
3.2 Writing Your Article
Once the keywords are chosen, they need to be mentioned in the
right places: in the title, and as often as possible in the abstract
and the body of the text (but, of course, not so often as to annoy
readers). Although in general titles should be fairly short, we
suggest choosing a longer title if there are many relevant
Synonyms of important keywords should also be mentioned a few
times in the body of the text, so that the article may be found by
someone who does not know the most common terminology used
in the research field. If possible, synonyms should also be
mentioned in the abstract, particularly because some academic
search engines do not index the document’s full text.
Be consistent in spelling people’s names, taking special care
with names that contain special characters. If names are used
inconsistently, search engines may not be able to identify articles
or citations correctly; as a consequence, citations may be
assigned incorrectly, and articles will not be as highly ranked as
they could be. For instance, Jöran, Joeran, and Joran are all
correct spellings of the same name (given different transcription
rules), but Google Scholar sees them as three different names.
The article should use a common scientific layout and structure,
including standard sections: introduction, related work, results,
and so on. A common scientific layout and structure will help
Web-based academic search engines to identify an article as
Academic search engines, and especially Google Scholar, assign
significant weight to citation counts. Citations influence whether
articles are indexed at all, and they also influence the ranking of
articles. We do not want to encourage readers to build citation
circles,’ or to take any other unethical action. But any published
articles you have read that relate to your current research paper
should be cited. When referencing your own published work, it is
important to include a link where that work can be downloaded.
This helps readers to find your article and helps academic search
engines to index the referenced article’s full text. Of course, this
can also be done for other articles that have well-known (i.e.,
stable and possibly canonical) download locations.
3.3 Preparing for Publication
Text in figures and tables should be machine readable (i.e.,
vector graphics containing font-based text should be used instead
Google Adwords;
Google keyword tool,
For example, keywords such as ‘Web’ and ‘HTML’ may be of
limited use because there are too many papers published in that
space, in which case it makes more sense to narrow the scope
and choose better-differentiated keywords.
of rasterized images) so that it can easily be indexed by academic
search engines. Vector graphics also look more professional, and
are more user friendly, than raster/bitmap graphics. Graphics
stored as JPEG, BMP, GIF, TIFF, or PNG files are not vector
When documents are converted to PDF, all metadata should be
correct (especially author and title). Some search engines use
PDF metadata to identify the file or to display information about
the article on the search results page. It may also be beneficial to
give a meaningful file name to each article.
3.4 Publishing
As part of the optimization process, authors should consider the
journal’s or publisher’s policies. Open-access articles usually
receive more citations than articles accessible only by purchase
or subscription; and, obviously, only articles that are available on
the Web can be indexed by Web-based academic search engines.
Accordingly, when selecting a journal or publisher for
submission, authors should favor those that cooperate with
Google Scholar and other academic search engines, since the
article will potentially obtain more readers and receive more
If a journal does not publish online, authors should
favor publishers who at least allow authors to put their articles
on their or their institutions’ home pages.
3.5 Follow-Up
There are three ways to optimize articles for academic search
engines after publication.
The first is to publish the article on the author’s home page, so
that Web-based academic search engines can find and index it
even if the journal or publisher does not publish the article
online. An author who does not have a Web page might post
articles on an institutional Web page or upload it to a site such as, which offers researchers a personal publications
home page that is regularly crawled by Google Scholar (and, of
course, by SciPlore Search). However, it is important to
determine that posting or uploading the article does not
constitute a violation of the author’s agreement with the
Second, an article that includes outdated words might be
replaced by either updating the existing article or publishing a
new version on the author’s home page. Google Scholar, at least,
considers all versions of an article available on the Web. We
consider this a good way of making older articles easier to find.
However, this practice may also violate your publisher’s
copyright policy, and it may also be considered misbehavior by
other researchers. It could also be a risky strategy: at some point
in the future, search engines may come to classify this practice as
spamming. In any case, updated articles should be clearly labeled
as such, so that readers are aware that they are reading a
modified version.
Third, it is important to create meaningful parent Web pages for
PDF files. This means that Web pages that link to the PDF file
should mention the most important keywords and the PDFs
The main criteria for selecting a publisher or journal, of
course, should still be its reputation and its general suitability
for the paper. The policy is to be seen as an additional factor.
metadata (title, author, and abstract). We do not know whether
any academic search engines are considering these data yet, but
normal search engines do consider them, and it seems only a
matter of time before academic search engines do, too.
As was true in the beginning for classic SEO, there are some
reservations about ASEO in the academic community. When we
submitted our study about Google Scholar’s ranking algorithm
[2] to a conference, it was rejected. One reviewer provided the
following feedback:
I’m not a big fan of this area of research […]. I know it’s in
the call for papers, but I think that’s a mistake.
A second reviewer wrote,
[This] paper seems to encourage scientific paper authors to
learn Google scholar’s ranking method and write papers
accordingly to boost ranking [which is not] acceptable to
scientific communities which are supposed to advocate true
technical quality/impact instead of ranking.
ASEO should not be seen as a guide on how to cheat academic
search engines. Rather, it is about helping academic search
engines to understand the content of research papers and, thus,
about how to make this content more widely and easily available.
Certainly, we can anticipate that some researchers will try to
boost their rankings in illegitimate ways. However, the same
problem exists in regular Web searching; and eventually Web
search engines manage to avoid spam with considerable success,
and so will academic search engines. In the long term, ASEO
will be beneficial for all authors, search engines, and users of
search engines. Therefore, we believe that academic search
engine optimization (ASEO) should be a common procedure for
researchers, similar to, for instance, selecting an appropriate
journal for publication.
We thank the SEO Bert van Heerde from Insyde
( for his valuable feedback, and Barbara
Shahin for proofreading this article.
The research career of Jöran Beel and Bela Gipp began about ten
years ago when they won second prize in Jugend Forscht,
Germany’s largest and most reputable youth science competition
and received awards from, among others, German Chancellor
Gerhard Schröder for their outstanding research work. In 2007,
they graduated with distinction at OVGU Magdeburg, Germany,
in the field of computer science. They now work for the VLBA-
Lab and are PhD students, currently at UC Berkeley as visiting
student researchers. During the past years they have published
several papers about academic search engines and research paper
recommender systems.
Erik Wilde is Adjunct Professor at the UC Berkeley School of
Information. He began his work in Web technologies and Web
architectures a little over ten years ago by publishing the first
book providing a complete overview of Web technologies. After
focusing for some years on XML technologies, XML and
modelling, mapping issues between XML and non-tree
metamodels, and XML-centric design of applications and data
models, he has recently shifted his main focus to information and
application architecture, mobile applications, geo-location issues
on the Web, and how to design data sharing that is open and
accessible for many different service consumers.
[1] Jöran Beel and Bela Gipp. Google Scholar’s Ranking
Algorithm: The Impact of Citation Counts (An Empirical Study).
In André Flory and Martine Collard, editors, Proceedings of the
3rd IEEE International Conference on Research Challenges in
Information Science (RCIS’09), pages 439446, Fez (Morocco),
April 2009. IEEE. doi: 10.1109/RCIS.2009.5089308. ISBN 978-
1-4244-2865-6. Available on
[2] Jöran Beel and Bela Gipp. Google Scholar’s Ranking
Algorithm: An Introductory Overview. In Birger Larsen and
Jacqueline Leta, editors, Proceedings of the 12th International
Conference on Scientometrics and Informetrics (ISSI’09),
volume 1, pages 230241, Rio de Janeiro (Brazil), July 2009.
International Society for Scientometrics and Informetrics. ISSN
2175-1935. Available on
[3] Jöran Beel and Bela Gipp. Google Scholar’s Ranking
Algorithm: The Impact of Articles’ Age (An Empirical Study). In
Shahram Latifi, editor, Proceedings of the 6th International
Conference on Information Technology: New Generations
(ITNG’09), pages 160164, Las Vegas (USA), April 2009. IEEE.
doi: 10.1109/ITNG.2009.317. ISBN 978-1424437702. Available
[4] Google. Google’s Search Engine Optimization Starter Guide.
PDF, November 2008. URL
[5] Albert Bifet and Carlos Castillo. An Analysis of Factors Used
in Search Engine Ranking. In Proceedings of the 14th
International World Wide Web Conference (WWW2005), First
International Workshop on Adversarial Information Retrieval on
the Web (AIRWeB’05), 2005.
[6] Michael P. Evans. Analysing Google rankings through search
engine optimization data. Internet Research, 17 (1): 2137, 2007.
doi: 10.1108/10662240710730470.
[7] Jin Zhang and Alexandra Dimitroff. The impact of metadata
implementation on webpage visibility in search engine results
(Part II). Cross-Language Information Retrieval, 41 (3): 691
715, May 2005.
[8] Harold Davis. Search Engine Optimization. O’Reilly, 2006.
[9] Jennifer Grappone and Gradiva Couzin. Search Engine
Optimization: An Hour a Day. John Wiley and Sons, 2nd edition,
[10] Peter Kent. Search engine optimization for dummies. Willey
Publishing Inc, 2006.
[11] AA Benczur, K Csalogány, T Sarlós, and M Uher.
SpamRank Fully Automatic Link Spam Detection. In
Adversarial Information Retrieval on the Web (AiRWEB’05),
[12] A. Benczúr, K. Csalogány, and T. Sarlós. Link-based
similarity search to fight web spam. Adversarial Information
Retrieval on the Web (AIRWEB), Seattle, Washington, USA,
[13] I. Drost and T. Scheffer. Thwarting the nigritude
ultramarine: Learning to identify link spam. Lecture Notes in
Computer Science, 3720: 96, 2005.
[14] D. Fetterly, M. Manasse, and M. Najork. Spam, damn spam,
and statistics: Using statistical analysis to locate spam web
pages. pages 16, 2004.
[15] Q. Gan and T. Suel. Improving web spam classifiers using
link structure. In Proceedings of the 3rd international workshop
on Adversarial information retrieval on the web, page 20. ACM,
[16] Z. Gyöngyi and H. Garcia-Molina. Link spam alliances. In
Proceedings of the 31st international conference on Very large
data bases, page 528. VLDB Endowment, 2005.
[17] H. Saito, M. Toyoda, M. Kitsuregawa, and K. Aihara. A
large-scale study of link spam detection by graph algorithms. In
Proceedings of the 3rd international workshop on Adversarial
information retrieval on the web, page 48. ACM, 2007.
[18] B. Wu and K. Chellapilla. Extracting link spam using biased
random walks from spam seed sets. In Proceedings of the 3rd
international workshop on Adversarial information retrieval on
the web, page 44. ACM, 2007.
[19] C. Castillo, D. Donato, A. Gionis, V. Murdock, and
F. Silvestri. Know your neighbors: Web spam detection using the
web topology. In Proceedings of the 30th annual international
ACM SIGIR conference on Research and development in
information retrieval, page 430. ACM, 2007.
[20] G.G. Geng, C.H. Wang, and Q.D. Li. Improving
Spamdexing Detection Via a Two-Stage Classification Strategy.
page 356, 2008.
[21] I.S. Nathenson. Internet infoglut and invisible ink:
Spamdexing search engines with meta tags. Harv. J. Law & Tec,
12: 43683, 1998.
[22] T. Urvoy, E. Chauveau, P. Filoche, and T. Lavergne.
Tracking web spam with HTML style similarities. ACM
Transactions on the Web (TWEB), 2, 2008.
[23] T. Urvoy, T. Lavergne, and P. Filoche. Tracking web spam
with hidden style similarity. In AIRWeb 2006, page 25, 2006.
[24] Masahiro Kimura, Kazumi Saito, Kazuhiro Kazama, and
Shin ya Sato. Detecting Search Engine Spam from a Trackback
Network in Blogspace. Lecture Notes in Computer Science:
Knowledge-Based Intelligent Information and Engineering
Systems, 3684: 723729, 2005. doi: 10.1007/11554028_101.
[25] Alexandros Ntoulas, Marc Najork, Mark Manasse, and
Dennis Fetterly. Detecting spam web pages through content
analysis. In 15th International Conference on World Wide Web,
pages 8392. ACM, 2006.
[26] Baoning Wu and Brian D. Davison. Identifying link farm
spam pages. In 14th International Conference on World Wide
Web, pages 820829, 2005.
[27] Yahoo! How do I improve the ranking of my web site in the
search results?, July 2007. URL
[28] Alex Chitu. Google’s Market Share in Your Country.
Website, March 2009. URL
2009/03/googles-market-share-in-your-country.html https://-
[29] D. Lewandowski and P. Mayr. Exploring the academic
invisible web. Library Hi Tech, 24 (4): 529539, 2006.
[30] Nisa Bakkalbasi, Kathleen Bauer, Janis Glover, and Lei
Wang. Three options for citation tracking: Google Scholar,
Scopus and Web of Science. Biomedical Digital Libraries, 3,
2006. doi: 10.1186/1742-5581-3-7.
[31] John J. Meier and Thomas W. Conkling. Google Scholar’s
Coverage of the Engineering Literature: An Empirical Study. The
Journal of Academic Librarianship, 34 (34): 196201, 2008.
[32] William H. Walters. Google Scholar coverage of a
multidisciplinary field. Information Processing & Management,
43 (4): 11211132, July 2007. doi:
[33] Google. About Google Scholar. Website, 2008. URL http://-
[34] Bert van Heerde. RE: Pre-print: Academic Search Engine
Optimization. Email, 3 September 2009.
[35] Google Scholar. Support for Scholarly Publishers. Website,
2009. URL
[36] S. Robertson, H. Zaragoza, and M. Taylor. Simple BM25
extension to multiple weighted fields. In Proceedings of the
thirteenth ACM international conference on Information and
knowledge management, pages 4249. ACM New York, NY,
USA, 2004.
... Finding relevant research literature in a very large online databases is a well-known challenge [4,3,35,18,1,14,24,40]. PubMed, arXiv, and other systems hold more than 50 million research papers on the world wide web as of 2010 [21]. ...
... Onetime search answers one query at a time, but does not exploit results of previous queries from the same user. It may exploit previous queries of other users (and from the same user without explicitly be aware to the fact this is the same user) to globally improve results [3]. Life-time search can exploit these results, but, in general, suffers from concept drift, as users switch between research topics intermittently [2,27,10]. ...
... Finding papers relevant to one's research is an every-day challenge for scientists around the globe [4,3,35,18,1,24]. Multiple technology approaches for searching have been offered, falling into two groups: one-time search (each query treated independently), and life-time search (results depend on previous queries). ...
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Finding relevant research literature in online databases is a familiar challenge to all researchers. General search approaches trying to tackle this challenge fall into two groups: one-time search and life-time search. We observe that both approaches ignore unique attributes of the research domain and are affected by concept drift. We posit that in searching for research papers, a combination of a life-time search engine with an explicitly-provided context (project) provides a solution to the concept drift problem. We developed and deployed a project-based meta-search engine for research papers called Rivendell. Using Rivendell, we conducted experiments with 199 subjects, comparing project-based search performance to one-time and life-time search engines, revealing an improvement of up to 12.8 percent in project-based search compared to life-time search.
... Indeed, a powerful industry is emerging to help content producers optimize their likelihood of "recommendation," while best practices across many sites of content production include considerations of encoded content delivery systems. For example, it behooves journalists, bloggers, and even acade mics to choose titles, keywords, and references/links that strategically connect one piece of work to popular topics and networks, thus bolstering the chances that an article, book, or post is delivered to potential consumers through automated recommender algorithms (Beel, Gipp, and Wilde 2010;Blanchett Neheli 2018;Christin 2018Christin , 2020. ...
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Through a theoretical and substantive review, the author argues that curation is a central thread that weaves together and underlies multiple and diverse elements of personal and public life. This argument is built on a theoretical scaffold which delineates curation as a situated matrix of production and consumption, networks, and code. Defined broadly as the discriminate selection and organization of materials, the author documents curation as it figures into identity processes, content moderation, news and information, and rec ommender systems. The curatorial matrix (production and consumption, networks and code) ties together distinct literatures from across internet and information studies. This places scholars in conversation with each other and demonstrates curation as a founda tional process in an environment marked by data saturation. The author first outlines cu ration as a theoretical construct and then reviews select works, rooting them in the cura torial matrix. Digital technologies-which both run on and generate data-have become integrated into personal, public, and institutional life. Data production is seamless and persistent as the data corpus exponentially expands. Gone is the problem of information scarcity, displaced by the challenge of information glut (Andrejevic 2013). Under these data-saturated condi tions, curation emerges as a central process. Broadly defined, "curation" is the discriminate selection and organization of materials. Humans are ontologically curatorial and always have been. The world is full of sensory stimuli that subjects filter, evaluate, highlight, background, and ignore. These are curato rial practices that rely on and reinforce shared meaning systems (Berger and Luckmann 1991; Blumer 1969; Zerubavel 1996). Indeed, curation is intrinsic to the human condition, far predating the "digital turn." However, in a networked and data-driven social context, curation is amplified, explicit, and acute.
... Apart from such "webometric" research, in recent years, there have been various studies of SEO specific to the academic sphere. These studies have dealt with questions such as the application of SEO techniques to the dissemination of academic production (Codina, 2017;Shahzad et al., 2017), the optimisation of academic articles in Google Scholar (Beel et al., 2009), the visibility of universities on academic social media platforms (French and Fagan, 2019;Gonz alez-Díaz et al., 2015) and the use of SEO in repositories or academic journals. However, there are very few studies dealing specifically with SEO and the visibility of universities on search engines. ...
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Purpose Universities play an important role in the promotion and implementation of the 2030 Agenda for Sustainable Development. This study aims to examine the visibility of information about the Sustainable Development Goals (SDGs) on the websites of Spanish and major international universities, by means of a quantitative and qualitative analysis with an online visibility management platform that makes use of big data technology. Design/methodology/approach The Web visibility of the universities studied in relation to the terms “SDG”, “Sustainable Development Goals” and “2030 Agenda” was determined using the SEMrush tool. Information was obtained on the number of web pages accessed and the queries formulated (query expansion). The content indexed by Google for these universities was compiled, and finally, the search engine optimization (SEO) factors applicable to the websites with the highest Web visibility were identified. Findings The universities analysed are content creators but do not have very high Web visibility in Web searches for information on the SDGs. Of the 98 universities analysed, only four feature prominently in search results. Originality/value Although research exists on the application of SEO to different areas, there have not, to date, been any studies examining the Web visibility of universities in relation to Web searches for information on the 2030 Agenda. The main contributions of this study are the global perspective it provides on the Web visibility of content produced by universities about the SDGs and the recommendations it offers for improving that visibility.
... which greatly increased the ability of researchers to make their articles available to academics public. Like other types of ranking search results, articles displayed in the top positions are more likely to be read [1]. The internet is available with hundreds of thousands of web pages, it's better to use a search engine to reach data that is following what is needed or information [2]. ...
... Optimization of resource indexing (e.g., Web of Science, Scopus, PubMed, etc.) and search engines (e.g., Google or Google Scholar) will aid the discovery of more "prominent" literature, even if it is not necessarily the "best" literature (Beel et al., 2010). This may be one reason to explain why "predatory publishing" literature in journals of low-quality or poor scholarly value that is published by exploitative or predatory entities/publishers, are a risk to academia (Teixeira da Silva et al., 2019), because the citation of improperly vetted or unscholarly literature amplifies the Matthew Effect. ...
The fame or status of an academic might not be built exclusively on research merit alone. In a world of competitive publishing, where vanity, metrics and citations play key roles in academics’ survival, for better or for worse, journal or institutional prestige may also serve as catalysts to further promote their status. The Matthew Effect, which breeds success from success, may rely on standing on the shoulders of others, citation bias, or the efforts of a collaborative network. Prestige is driven by resource, which in turn feeds prestige, amplifying advantage and rewards, and ultimately skewing recognition.
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The evolution of marketing concepts in recent years has made possible the use of various contents that are perceptible, comprehensible, and appreciable by customers. Such avenues are tailored to suit the needs and expectations of the customers in the digital era of content marketing in business. In essence, business concerns could aspire to design a dependable platform using engaging content to attract customers by satisfying and retaining them for future business growth and development. The purpose of this study is to examine the effect of digital content marketing in directing traffic to a particular content owner through an increased level of customer loyalty. The study defines and appraises the concept of digital content marketing and the effect of engaging marketing content on customer loyalty. The respondents for this study were postgraduate students of Ahmadu Bello University, Nigeria. The study designed and administered some digital Content marketing-related questions that could induce customer loyalty to respondents. The data obtained from the study were analyzed using the Pearson Correlation Coefficient technique; the study revealed that the accomplishment of unambiguous and dependable content by business concerns could increase the delight of organizational customers. The analysis of data revealed that there is a link between digital content marketing and customer loyalty in Nigerian Universities. In conclusion, the study appraises the need for effective dissemination of information through digital marketing content to encourage end-users to break away from the grip of traditional marketing platforms and embrace the new concept of digital content marketing in Nigeria.
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Professional managers are key constituents of the human resources in a country. Human resources have the distinct skills to combine every other factor of production in a way that turns around a nation’s status of under-development to that of development. The state contributes by establishing institutions such as the Institute of Management to manage and apply factors of production in the most fruitful path to development. To ensure professional managers and human resources have integrity to promote transparency in the corporate world, Institutes of Management requires both academic and good character qualifications of professional managers. The work, however, establishes that notwithstanding the positive contributions of professional managers and human resources to development, Nigeria is not visible in the development chart on account of absence of enabling environment, ease of doing business and eventual membership of the Group of Twenty.
Fuzzy real options analysis has advanced greatly during the last decade, specifically, through the development of so-called fuzzy pay off techniques. The practicality and intuitiveness of these methods allow their straightforward integration into any spreadsheet or other evaluation systems for easy applications of real options thinking to real investments and strategies in industry and to public policy. Real options valuation models are capable to reflect the value of flexibility, i.e., the inherent optional possible actions, which managers can take during the investment period or in public policy settings. Traditional NPV methods cannot value such optionalities. Fuzzy modeling is shown to account for high uncertainty and imprecision under which an expert evaluation is conducted. This paper generalizes the credibilistic pay-off method for real options valuation using interval valued fuzzy numbers, IVFNs, by means of mλ-measure for the optimism–pessimism level of an expert analyst. The mλ-measure is defined using necessity and possibility measures to correspond to the optimism–pessimism level. Real options values, ROVs, will be obtained using the λ-parameter and fuzzy numbers. Similarly, ROVs are obtained using IVFNs. This paper introduces a novel credibilistic real options model, which is based on the optimism–pessimism measure and IVFNs. The model outcomes are compared to the original credibilistic real options model through a numerical case example in a merger and acquisition context. *** Cite this paper as: Kinnunen J., Georgescu I. (2022) Interval-Valued Credibilistic Real Options Modeling Under Optimism-Pessimism Level. In: Saraswat M., Roy S., Chowdhury C., Gandomi A.H. (eds) Proceedings of International Conference on Data Science and Applications. Lecture Notes in Networks and Systems, vol 288. Springer, Singapore. *** Indexed by Scopus /Jufo 1 ***
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Peer review and citation analysis are the two most common approaches for quality evaluations of scientific publications, although they are subject to criticism for various reasons. This paper outlines the problems of citation analysis and peer review and introduces Collaborative Document Evaluation as a supplement or possibly even a substitute. Collaborative Document Evaluation aims to enable the readers of publications to act as peer reviewers and share their evaluations in the form of ratings, annotations, links and classifications via the internet. In addition, Collaborative Document Evaluation might well enhance the search for publications. In this paper the implications of Collaborative Document Evaluation for the scientific community are discussed and questions are asked as to how to create incentives for scientists to participate.
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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.
Conference Paper
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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 results of our first study were confirmed: Citation counts is the highest weighed factor in Google Scholar's ranking algorithm. Highly cited articles are found significantly more often in higher positions than articles that are cited less often. Therefore, Google Scholar seems to be more suitable for searching standard literature than for gems or articles by authors advancing a view different from the mainstream. However, interesting exceptions for some search queries occurred. In some cases no correlation existed; in others bizarre patterns were recognizable, suggesting that citation counts sometimes have no impact at all on articles' rankings.
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This paper evaluates the content of Google Scholar and seven other databases (Academic Search Elite, AgeLine, ArticleFirst, GEOBASE, POPLINE, Social Sciences Abstracts, and Social Sciences Citation Index) within the multidisciplinary subject area of later-life migration. Each database is evaluated with reference to a set of 155 core articles selected in advance—the most important studies of later-life migration published from 1990 to 2000. Of the eight databases, Google Scholar indexes the greatest number of core articles (93%) and provides the most uniform publisher and date coverage. It covers 27% more core articles than the second-ranked database (SSCI) and 2.4 times as many as the lowest-ranked database (GEOBASE). At the same time, a substantial proportion of the citations provided by Google Scholar are incomplete (32%) or presented without abstracts (33%).
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This paper describes a simple way of adapting the BM25 ranking formula to deal with structured documents. In the past it has been common to compute scores for the individual fields (e.g. title and body) independently and then combine these scores (typically linearly) to arrive at a final score for the document. We highlight how this approach can lead to poor performance by breaking the carefully constructed non-linear saturation of term frequency in the BM25 function. We propose a much more intuitive alternative which weights term frequencies before the non-linear term frequency saturation function is applied. In this scheme, a structured document with a title weight of two is mapped to an unstructured document with the title content repeated twice. This more verbose unstructured document is then ranked in the usual way. We demonstrate the advantages of this method with experiments on Reuters Vol1 and the TREC dotGov collection.
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This paper investigates the influence of different page features on the ranking of search engine results. We use Google as our testbed and analyze the result rankings for several queries of different categories using statistical methods. We reformulate the problem of learning the underlying, hidden scores as binary classification. To this problem we then apply both linear and non-linear methods. In all cases, we split the data into a training set and a test set to obtain a meaningful, unbiased estimator for the quality of our predictor. Although our results clearly show that the scoring function cannot be approximated well using only the observed features, we do obtain many interesting insights along the way and discuss ways of obtaining a better estimate and principal limitations in trying to do so. 1
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Owner: a-beel, Added to JabRef: 2009.02.24
textlessptextgreatertextlessbrtextgreaterGoogle Scholar's coverage of the engineering literature is analyzed by comparing its contents with those of Compendex, the premier engineering database. Records retrieved from Compendex were searched in Google Scholar, and a decade by decade comparison was done from the 1950s through 2007. The results show that the percentage of records appearing in Google Scholar increased over time, approaching a 90 percent matching rate for materials published after 1990.textless/ptextgreater
This paper discusses the impact of metadata implementation in a webpage on its visibility performance in a search engine results list. Influential internal and external factors of metadata implementation were identified. How these factors affect webpage visibility in a search engine results list was examined in an experimental study. Findings suggest that metadata is a good mechanism to improve webpage visibility, the metadata subject field plays a more important role than any other metadata field and keywords extracted from the webpage itself, particularly title or full-text, are most effective. To maximize the effects, these keywords should come from both title and full-text.
Purpose – The purpose of this paper is to identify the most popular techniques used to rank a web page highly in Google. Design/methodology/approach – The paper presents the results of a study into 50 highly optimized web pages that were created as part of a Search Engine Optimization competition. The study focuses on the most popular techniques that were used to rank highest in this competition, and includes an analysis on the use of PageRank, number of pages, number of in-links, domain age and the use of third party sites such as directories and social bookmarking sites. A separate study was made into 50 non-optimized web pages for comparison. Findings – The paper provides insight into the techniques that successful Search Engine Optimizers use to ensure a page ranks highly in Google. Recognizes the importance of PageRank and links as well as directories and social bookmarking sites. Research limitations/implications – Only the top 50 web sites for a specific query were analyzed. Analysing more web sites and comparing with similar studies in different competition would provide more concrete results. Practical implications – The paper offers a revealing insight into the techniques used by industry experts to rank highly in Google, and the success or otherwise of those techniques. Originality/value – This paper fulfils an identified need for web sites and e-commerce sites keen to attract a wider web audience.