# Google Scholar is manipulatable

Hazem Ibrahim,1,2,‡ Fengyuan Liu,1,3,‡ Yasir Zaki1,∗ Talal Rahwan1,∗

1Department of Computer Science, New York University Abu Dhabi, UAE
2Tandon School of Engineering, New York University, New York, USA
3Courant Institute of Mathematical Sciences, New York University, New York, NY
10012, USA
‡ Joint first authors
∗To whom correspondence should be addressed; E-mails: {talal.rahwan,
<EMAIL_ADDRESS>
> Citations are widely considered in scientists’ evaluation. As such,
> scientists may be incentivized to inflate their citation counts. While
> previous literature has examined self-citations and citation cartels, it
> remains unclear whether scientists can purchase citations. Here, we compile
> a dataset of $\sim$1.6 million profiles on Google Scholar to examine
> instances of citation fraud on the platform. We survey faculty at highly-
> ranked universities, and confirm that Google Scholar is widely used when
> evaluating scientists. Intrigued by a citation-boosting service that we
> unravelled during our investigation, we contacted the service while
> undercover as a fictional author, and managed to purchase 50 citations.
> These findings provide conclusive evidence that citations can be bought in
> bulk, and highlight the need to look beyond citation counts.

## Significance statement

In this study, we analyze over 1.6 million Google Scholar profiles and
identify instances of citation manipulation to boost an author’s citation
metrics. In many cases, this was done through a previously undocumented
practice of “citation purchasing”, where an author pays a small fee to a third
party company which provides citations to the author in bulk. We confirm that
this practice is possible by purchasing citations to a fictional author. This
type of manipulation is of particular concern due to Google Scholar’s wide-
spread use in scientist evaluation processes, which we confirm with a survey
of faculty from top-ranked universities. Our findings bring to light new forms
of citation manipulation and emphasize the need to look beyond citation
counts.

## Introduction

Due to the nature of scientific progress, scientists build on, and cite, the
work of their predecessors to acknowledge their intellectual contributions
while advancing the frontiers of human knowledge (?). However, the purpose of
citations has evolved beyond this initial purpose, with it arguably becoming a
synonym for scientific success (?, ?). To date, citation-based measures (e.g.,
h-index, i10-index, journal impact factors) dominate the evaluation of papers
(?), scientists (?) and journals (?), despite the ongoing debate surrounding
their use (?, ?, ?).

One reason behind using these measures is the fierce competition that
scientists face when competing for limited resources such as grants,
laboratory space, tenure, and the brightest graduate students (?), the
allocation of which requires a measure of performance (?). In this process,
bibliometric data such as citation counts have become an indispensable
component of scientific evaluation for at least the past four decades (?), and
has become one of the primary metrics which scientists strive to maximize (?).
While some may consider such a competitive environment to be a force for good
(?), others argue that it creates perverse incentives for scientists to “game
the system”, with many resorting to various methods to boost their citation
counts, e.g., via self-citations (?, ?), citation cartels (?, ?, ?, ?), and
coercive citations (?, ?, ?, ?). Together, these practices are known as
citation manipulation or reference list manipulation. Unfortunately, these
practices are hard to define due the fact that the line between innocuous and
malicious practices is not always clear. For example, every scientist builds
upon their own research or the work of their collaborators, so everyone is
expected to self-cite or frequently exchange citations with their
collaborators. As a result, it is virtually impossible to determine with
absolute certainty whether someone cites out of necessity or simply to game
the system.

Perhaps the most egregious approach to manipulating one’s citations would be
to purchase them outright. Anecdotes of emails asking authors to cite articles
in return for a fee have been exposed (?, ?), although it is unclear whether
these emails were simply scams. As such, it remains unclear whether it is
possible for any given scientist to purchase citations to their own work. For
such a transaction to take place, it requires at least two culprits that are
research-active scientists—the one who purchases the citations, and the one
who plants them in their own articles in return for a fee; a possible third
culprit would be an individual or company who brokers this transaction.

Against this background, we set out to understand the proportion of faculty
members in highly-ranked universities who use citation metrics when recruiting
or promoting candidates, and the databases they use to retrieve these metrics.
To do so, we surveyed over 30,000 faculty members of the 10 highest ranked
universities globally. We found that the majority of faculty members indeed
consider citation counts, and Google Scholar is the most popular source to
retrieve such information, beating all other databases combined. Surprisingly,
despite the popularity of Google Scholar, it has received little attention in
studies investigating scientists and their citations, compared to other
databases such as Web of Science (?, ?, ?, ?, ?, ?, ?, ?), USPTO (?, ?, ?),
Microsoft Academic Graph (?, ?, ?, ?, ?, ?), or discipline-specific databases
(?, ?, ?, ?, ?, ?, ?, ?), possibly due to the lack of an accessible dataset
which compiles all information on Google Scholar. Motivated by this finding,
we steered our analysis of “citation boosting” towards Google Scholar and
curated a dataset of over 1.6 million profiles on this platform. Using various
measures of citation irregularity, we found several scientists with suspicious
profiles that may have engaged with citation boosting services. Following this
trail, we indeed found such a company that provided citations to one of the
suspicious scientists. Before engaging with this company, we generated 20
research articles using a Large Language Model while listing a fictional
character as an author. We then created a Google Scholar profile for that
character, and contacted the company to ask them to boost the citations of
this profile. Indeed, we managed to purchase 50 citations, proving that
citations can indeed be bought in bulk for a relatively small fee in a matter
of weeks. Together, our findings question the reliance on Google Scholar to
evaluate scientists by demonstrating its susceptibility to manipulation, while
contributing more broadly to the plea of reducing the reliance on citation
metrics when evaluating scientists.

## Results

### Google Scholar is a prominent source of citation metrics

To gauge the extent to which academics consider citations when evaluating
scientists for recruitment or promotion purposes, we surveyed faculty members
of the 10 most highly ranked universities around the world (see Methods for
more details). The results of this survey are summarized in Figure 1. We find
that the majority of faculty consider the number of citations when evaluating
candidates (Figure 1A). Moreover, out of those who consider citations, over
60% obtain this data from Google Scholar, making it more popular than all
other sources combined (Figure 1B, solid bars). Participants were also asked
about the source of citation metrics they believe their colleagues use the
most. Again, Google Scholar is the most popular source, selected by 73% of the
participants (Figure 1B, hatched bars).

Figure 1:  Survey responses from faculty of the top-10 ranked universities
around the world. A, The percentage of faculty who consider citations when
evaluating candidates (blue) and those who do not (red). B, Solid bars
indicate, out of those who self-report considering citations when evaluating
candidates, the percentage of faculty using each database as the primary
source of citation metrics. Hatched bars indicate, out of those who report
that their colleagues consider citations when evaluating candidates, the
percentage of colleagues using each database. C, Relative to the Natural
Sciences, the percentage of faculty from each group of disciplines who
consider citations when evaluating candidates. D, Relative to the Natural
Sciences, the percentage of faculty who use Google Scholar as the primary
source of citation metrics. In (C) and (D), dots denote OLS-estimated
coefficients and error bars represent 95% confidence intervals.

Next, we investigate whether the usage of citation metrics and Google Scholar
differ across disciplines. To this end, we asked participants to select their
discipline from a pre-determined list, or select “Other” if they did not
identify with any. We then grouped the disciplines into four categories,
namely, Natural Sciences, Social Sciences, Computational Sciences, and
Humanities. As can be seen in Figure 1C, we do not find evidence that
disciplines differ in their likelihood to consider citations when evaluating
candidates. However, when it comes to the usage of Google Scholar, we find
significant differences across disciplines (Figure 1D). Faculty in the
Computational Sciences and Social Sciences are more likely to use Google
Scholar as their primary source of citation metrics, compared to those in the
Natural Sciences ($p<0.001$).

Overall, our survey indicates that citations are often used by faculty members
when evaluating scientists for recruitment or promotion purposes. Moreover, it
establishes for the first time that Google Scholar is by far the most popular
source of citation metrics used for this purpose.

### Investigating suspicious citations

Our findings thus far suggest that scientists are incentivized to increase
their citations on Google Scholar. Next, we investigate whether scientists can
manipulate their Google Scholar profiles by planting fake citations. Here, we
follow a proof-by-example approach. That is, our goal is to identify a few
examples of Google Scholar profiles that seem highly suspicious, thereby
demonstrating that the platform can indeed be manipulated by scientists
without having their profiles suspended. To this end, we identified five
suspicious authors whose Google Scholar profiles exhibit highly irregular
patterns, and compared them against authors who are similar in terms of their
field of research, academic birth year, number of publications, and number of
citations; see Methods for more details on how the suspicious authors and
their matches are identified.

The results of the comparison between suspicious authors (red) and their
matches (blue) are summarized in Figure 2. We begin our analysis by comparing
their annual citation rates prior to the year in which they reached their peak
number of citations (Figure 2A). Here, to unify the scale across the authors,
we only depict their number of citations relative to their peak (see
Supplementary Figure 1 for the absolute numbers). As can be seen, the matched
authors exhibit a gradual increase in citations prior to their peak, whereas
the suspicious authors exhibit a sudden spike in their citations in their peak
year.

Figure 2: A comparative analysis of suspicious authors and their matches. In
each plot, red lines and red dots denote suspicious authors, while blue ones
denote their matches. A, For the 4 years leading up to an author’s peak
citations, the annual number of citations relative to the peak. B, Discrepancy
between Google Scholar and Scopus in terms of the author’s citation count in
their peak year. In (C)-(H), for any given author, we focus on their 10
“citing papers”, i.e., the ones that reference them the most. C, The number of
references to a given author in each of their 10 citing papers. D, The total
number of references pointing to an author from their 10 citing papers,
divided by the number of the author’s unique papers being cited therein. E,
The citation network between the 10 citing papers ($\star$) and an author’s
papers ($\circ$) for a suspicious author (red) and their matching author
(blue). F, For each value $v$ on the x-axis, how many of the citing papers
have $v\%$ of their references pointing to the author in question. G, For each
value $v$ on the x-axis, how many of the citing papers have an average of $v$
references per page pointing to the author in question. H, For each value $v$
on the x-axis, how many of the citing papers have $v\%$ of their references
pointing to the author in question and are not referenced in the citing
papers’ main text despite being listed in its bibliography.

Next, we compare the authors’ citation counts in their peak year as listed in
Google Scholar vs. Scopus (Figure 2B), since the latter only indexes journals
that are reviewed and selected by an independent Content Selection and
Advisory Board (?). While it is natural to see a drop in citation counts due
to the more restrictive indexing by Scopus, suspicious authors on average
experience a citation drop of 96% on Scopus, while normal authors on average
experience a drop of 43%. In other words, not only do the suspicious authors
experience an unexpected increase in their citation counts in a particular
year, but more than 96% of those citations cannot be found on Scopus.

Our next analysis focuses on the 10 papers that contain the highest number of
citations to each author, hereinafter referred to as the “citing papers”. As
can be seen in Figure 2C, suspicious authors are referenced up to 45 times in
a citing paper, while their matches are referenced no more than 15 times.
Additionally, the variance in the number of times a suspicious author is
referenced by citing papers is suspiciously small. For example, the first
suspicious author is referenced exactly 45 times by all their citing papers.
It would be even more suspicious if all citing papers are referencing the same
45 papers of the author, rather than each of them citing 45 different papers
of that author. To determine whether this is the case, Figure 2D depicts the
total number of references pointing to an author from their 10 citing papers,
divided by the number of the author’s unique papers being cited therein. As
can be seen, for suspicious authors, this ratio is very close to 10,
indicating that all references are indeed directed towards the same set of
papers. On the other hand, the ratio is much smaller for the matched
scientists. To better illustrate this phenomenon, Figure 2E shows two
bipartite graphs of the 10 citing papers (stars) and the set of unique
authored papers (circles) for the suspicious author #1 and their match. Here,
the left panel is a complete bipartite graph, indicating that all 10 citing
papers are referencing the exact same set of 45 papers, while the right panel
is far from complete.

One possible explanation of the discrepancy in citations between suspicious
authors and their matches in Figure 2C is that the citing papers of the
suspicious authors have more pages and/or have more references, compared to
those of the matched authors. However, we find that this is not the case. More
specifically, the suspicious authors tend to receive much more citations even
when accounting for the total number of references in the bibliography (Figure
2F) and the total number of pages (Figure 2G) in the citing papers. Also
noteworthy is the fact that the percentage of the references to a suspicious
author reaches as high as 90% in one of the citing papers; see Supplementary
Appendix 1 for a redacted version of that paper.

Finally, in many citing papers, the references made to a suspicious author are
nowhere to be found in the main text, but only in the bibliography. As shown
in Figure 2H, in 18 of the 50 papers analyzed for the suspicious authors, all
of the references made to the author do not appear in the main text,
indicating that all those references made to the suspicious authors are
irrelevant to the content of the citing paper, which in turn suggests that
they were planted to boost the citation count of the suspicious author. For
example, one citing paper consists of only two pages, with just a single
reference in its main text, but references the suspicious author 29 times in
its bibliography; see Supplementary Appendix 2 for a redacted version of that
paper.

Considering that suspicious authors tend to receive an excessive number of
citations from a small set of papers, we propose a metric to highlight
potentially suspicious profiles. In particular, we introduce the citation
concentration index, or $c^{2}$-index for short. Specifically, the
$c^{2}$-index of a scientist is the largest number $n$ such that there exists
$n$ papers that each cite the scientist at least $n$ times. For a scientist
with a $c^{2}$-index of $n$, we also calculate the percentage of citations
that this scientist receives from the papers that cite them at least $n$ times
(henceforth, the $c^{2}$-percentage). To understand the distribution of
scientists across these two dimensions, we randomly select 100 Google Scholar
profiles from each of the nine disciplines with the largest number of authors
in our dataset, and calculate their $c^{2}$-index and $c^{2}$-percentage. The
results of this analysis are illustrated in Figure 3A. As can be seen, the
vast majority of scientists fall in the bottom right corner of the
distribution, since they are cited at most once by any citing paper. Very few
scientists have a $c^{2}$-index higher than 10, with the highest $c^{2}$-index
observed in the random sample being 25. However, among the suspicious authors,
the lowest $c^{2}$-index is 23, while the highest is 45, i.e., there exists 45
different papers which cite this author at least 45 times each. Hence, this
analysis confirms that all five suspicious authors highlighted in Figure 2
have unusually high $c^{2}$-indices, suggesting that the $c^{2}$-index could
help identify potentially suspicious profiles.

Figure 3: The distribution of $c^{2}$-index. A, A sample of 900 authors
randomly selected from the nine disciplines with the largest number of authors
in our dataset (100 per discipline). Triangles highlight the five suspicious
authors whose Google Scholar profiles exhibit highly irregular patterns. B,
Distribution of all scientists in Microsoft Academic Graph (MAG) who are cited
at least 100 times. For a scientist whose $c^{2}$-index is $n$ ($y$-axis), the
$x$-axis shows the percentage of citations a scientist receives from papers
citing them at least $n$ times. Inset shows the cumulative distribution of
$c^{2}$-index of all scientists (blue), Nobel laureates (purple), and top-10
most cited scientists in the following three fields on Google Scholar
(orange): machine learning, neuroscience, and bio-informatics. Red triangles
highlight scientists who were previously reported to have engaged in academic
misconduct.

So far, we only examined the $c^{2}$-index of a small set of authors on Google
Scholar. This is due to the expensive nature of collecting citation
information on Google Scholar, requiring a number of requests on the order of
the total number of citations of the set of authors to retrieve the necessary
information to compute the index. As such, to apply this index at scale, we
resort to the Microsoft Academic Graph (MAG) dataset, which includes over 200
million scientific papers published since the year 1800. Importantly, the
indices computed via MAG should be taken as a lower bound on the indices
computed via Google Scholar. This is not only due to the fact that MAG has
been deprecated since the end of 2021, but also due to MAG’s stricter indexing
policy (e.g., MAG does not include citations stemming from a document uploaded
on ResearchGate or OSF).

Figure 3B shows the distribution of all scientists with at least 100 citations
along two dimensions—their $c^{2}$-index and $c^{2}$-percentage according to
MAG. As can be seen, the boundary of the distribution resembles a hyperbola—on
the one hand, the vast majority of scientists are cited only once by all
citing papers, while on the other hand, successful scientists (i.e., Nobel
laureates or highly-cited scientists) tend to have high $c^{2}$-indices but
low $c^{2}$-percentages. Notice that a scientist may have a high $c^{2}$-index
if they produce a body of impactful works that are often cited together.
However, even for these extremely successful scientists, the total number of
citations that come in bulk rarely goes beyond 10% of their total number of
citations (98.6-th percentile value), and never exceeds 20%. As a result, it
is rare to have a high $c^{2}$ index (the 99-th percentile value of
$c^{2}$-index is 12, as can be seen in Figure 3B inset), and extremely rare to
have both a high $c^{2}$-index and a high $c^{2}$-percentage simultaneously.

Meanwhile, there exists a small group of scientists who not only received
citations in bulk from a large number of papers (i.e., having a high
$c^{2}$-index), but also received a large percentage of their citations in
this manner, raising the possibility that some of these citations may not be
genuine. To quantify the number of scientists who have a high $c^{2}$-index
and a high $c^{2}$-percentage simultaneously, we calculate an adjusted
$c^{2}$-index by multiplying the $c^{2}$-index of a scientist by the
$c^{2}$-percentage of that scientist. An example of a scientist with an
adjusted $c^{2}$-index of 4 could be a scientist with a $c^{2}$-index of 40
(i.e., there are 40 papers that each cite this scientist at least 40 times)
and a $c^{2}$-percentage of 10% (i.e., the citations received from those 40
papers constitute one tenth of all citations accumulated by that scientist
throughout their entire career). In other words, 1 in every 10 of their total
citations stem from a paper that cites them 40 times or more. As for
scientists with an adjusted $c^{2}$-index of 2, one such example could be
those with a $c^{2}$-index of 25 and a $c^{2}$ percentage of 8%. The dotted
lines in Figure 3B show the cut-offs with varying adjusted $c^{2}$-indices.
Across MAG, the adjusted $c^{2}$-index is $\geq$ 2 for 14,441 scientists,
$\geq$ 3 for 3,525 scientists, $\geq$ 4 for 1,161 scientists, and $\geq$ 5 for
485 scientists. Supplementary Figure 2 depicts the distribution of adjusted
$c^{2}$-index in MAG. As can be seen, the distribution resembles a straight
line on a log-log scale, which is the signature of a power-law distribution.
Finally, let us comment on Nobel laureates. As can be seen in Figure 3B, while
some Nobel laureates have high $c^{2}$-indices, exceeding 40 in some cases,
none of them have an adjusted $c^{2}$-index greater than 2.

To demonstrate that the adjusted $c^{2}$-index can serve as an indicator of
scientists who may have engaged in questionable practices to accumulate
citations, we examined four scientists who have been involved in public
scandals, to determine whether they have high adjusted $c^{2}$-indices. More
specifically, these four scientists either (1) repeatedly manipulated the
peer-review process as an editor to gain citations (?); (2) engaged in extreme
self-citing practices (?); (3) manipulated the peer-review process as a guest
editor which resulted in the retraction of hundreds of papers in bulk (?); or
(4) engaged in extreme self-publishing behaviour by publishing more than 150
papers in a journal that they edit (?). We examined the adjusted
$c^{2}$-indices of these scientists, and found all of them to be greater than
five; see the red stars in Figure 3B.

It is important to note that a high adjusted $c^{2}$-index does not
necessarily signal misconduct; after all, a pioneer in their field could end
up being cited tens of times per paper. Nevertheless, as we have demonstrated
through real examples of public scandals, a high adjusted $c^{2}$-index could
serve as a warning sign of citation manipulation. Next, we investigate the
possible ways in which citations could be manipulated on Google Scholar.

### Building a fictional Google Scholar profile

We examined the 114 authors in our dataset whose Google Scholar profiles
exhibit highly irregular patterns, and found that they received citations from
non-peer reviewed sources more frequently than regular authors; see
Supplementary Figure 4. More specifically, we find that pre-print servers,
namely, arXiv (?), Authorea (?), OSF (?), and ResearchGate (?) accounted for a
sizable proportion of their non-self citations received by the authors in our
dataset.

Motivated by this observation, we investigated the ease by which one may boost
their citations by uploading low-quality papers on pre-print servers. To this
end, instead of following a proof-by-example approach (as we have done in our
previous analysis of the five suspicious authors), we now create a Google
Scholar profile of a fictional character affiliated with a fictional
university, whose areas of interest included “Fake News”. Moreover, we
generated 20 research articles on the topic of “Fake News” using a large
language model, namely ChatGPT (?), while listing the character as an author.
These articles were generated using prompts such as “Generate a research
article abstract on the topic of Fake News” and “Generate the introduction for
an article with the following abstract”. These articles only included
references to each other as to not artificially boost the citations of any
real scientist.

Next, we attempted to upload these articles on each of the aforementioned pre-
print servers, with different levels of success due to the varying levels of
content moderation performed by these servers. In particular, there are three
stages of verification that a pre-print server may perform—when creating a
scientist’s profile, when uploading a manuscript, and after a manuscript is
made publicly available. Authorea and OSF do not perform any verification
during account registration. As for ResearchGate, it requires proof of
research and academic activity during account registration, although, based on
our experience, it does not verify the truthfulness of such proof. Finally,
arXiv requires newly created accounts to be linked to an email address of a
credible research institution, or to be endorsed by another scientist on the
platform, before being permitted to upload a manuscript. As a result, we were
able to create profiles linked to the fictional author on all of the servers
apart from arXiv. Once the accounts were created, we attempted to upload the
generated articles to the respective servers. Both Authorea and OSF performed
some pre-publishing moderation on these submitted articles, and both accepted
the submissions after two days. ResearchGate, on the other hand, did not
perform any moderation, and accepted the articles instantly. Two weeks after
the articles were made publicly available, Authorea deleted the account and
all associated articles, unlike the other servers.

Upon uploading 20 ChatGPT-generated articles to three different pre-print
servers, Google Scholar detected the references in these articles and
attributed them to the fictional character that we had created. Furthermore,
in some of the 20 generated articles, we planted bibliography entries to non-
existing papers, i.e., to papers that are supposedly authored by the fictional
character but were never generated nor uploaded onto any server. Surprisingly,
those non-existing papers were also indexed by Google Scholar, and the
citations that these papers received were reflected in the author’s total
number of citations on the platform. As a result, the profile page indicated
that the character had 380 citations in total, with an h-index of 19.
Moreover, in the research area of “Fake News”, the character was listed as the
36th most cited scientist on the platform. It should be noted that Google
Scholar neither censored the profile, nor reached out for further
verification, despite the numerous red flags: (i) having an affiliation that
does not exist; (ii) 100% of their 380 citations are stemming from their own
papers; and (iii) all of their papers are ChatGPT-generated without any
scientific contributions whatsoever.

Importantly, we find that the citations stemming from the articles on Authorea
persist on Google Scholar even after these articles had been removed from
Authorea’s server. The implication here is critical, as those wishing to
inflate their citation counts can upload fraudulent articles onto pre-print
servers and subsequently delete them once the citation has been indexed by
Google Scholar, essentially destroying any evidence of malpractice in the eyes
of the casual observer browsing the website. Indeed, we find that for the five
suspicious authors analyzed in the previous section, 32% of the articles which
cite them are no longer available on the servers they were once hosted on.
Whether this is due to post-moderation done by the hosting service or
intentional deletion by the authors is unclear. Regardless, these citations
remain on Google Scholar and continue to contribute to the authors’ citation
metrics.

### Citations can be bought

While investigating malicious citation boosting on Google Scholar, we
identified a ResearchGate account which provides many superfluous citations to
suspicious author #1, i.e., the author corresponding to the network
illustrated in Figure 2E. More specifically, the ResearchGate account hosted
many of the low-quality papers which contained excessive citations to
suspicious author #1. Further analysis revealed that this particular account
is affiliated with a website offering a so-called “H-index & Citations
Booster” service; see Supplementary Figure 3A for a screenshot of the website.
Intrigued by this statement, we set out to investigate whether citations can
indeed be bought through this website. To this end, we contacted an email
address listed on the website, and managed to purchase 50 citations to the
fictional character that we created; see Methods for more details on how
citations were purchased. These citations are emanated from 5 papers, which
are henceforth referred to as the “citing papers”.

For each of the five citing papers, we analyzed the bibliography and
quantified the number of times a given author is referenced; the result is
summarized in Supplementary Figure 3C. As can be seen, several other authors,
apart from our fictional character, also received seemingly purchased
citations emanating from these 5 papers. For instance, one author received 23
identical citations from 3 of the citing papers, while another received 18
identical citations from all 5 citing papers. This suggests that the company
may be processing purchased citations in bulk, planting citations purchased by
multiple people in the same set of papers. Furthermore, Supplementary Figure
3D depicts the proportion of references in the bibliography of these five
citing papers that do not appear in their main text. As can be seen, none of
the entries in the reference lists of two of the five papers appeared in their
main text. The remaining papers also fail to include a majority of the
citations listed in their bibliographies in their main text, with 90%, 73%,
and 69% of the citations not being included, respectively.

Considering that 4 of the 5 papers which planted references to our fictional
author stemmed from the same journal, and that planted citations to other
authors were also found in these papers, it is natural to wonder whether other
papers in this journal could also contain planted citations. To this end, we
collect all papers published in this journal so far in this year, and identify
the references that appear in more than one paper. We then construct a network
where the nodes represent the papers, and an edge connects a pair of papers if
they share at least one reference. Additionally, the weight of an edge denotes
the number of shared references between a pair of papers. Assuming that
planted citations appear in bulk in more than one paper, this would result in
clusters of papers with many shared references. While it is natural to see
pairs of papers with several shared references, particularly if they are on
the same topic, focusing on the set of papers sharing an extremely high number
of common references would potentially reveal the citations purchased in bulk.

As can be seen in Figure 4, several connected components emerge due to papers
which share multiple references. In particular, we highlight five clusters
containing papers that include a suspiciously high number of citations to a
given author. The papers which provided citations to the fictional author
created in the previous section are found in Cluster #4, but the fictional
author is far from the most extreme case in the network. For instance, in
Cluster #1, Author A.J. received citations to the same set of 30 papers which
he authored from 5 different papers in that cluster. Similarly, in Cluster #2,
Author M.K. received citations to the same set of 33 of their papers from 8
different papers published in the journal. These results suggest that
purchased citations within this journal are likely not limited to only our own
purchase of 50 citations.

Figure 4: The citation matching network of the journal that provided purchased
citations. Nodes denote papers published in this journal, and edges link two
papers which share at least one reference in their bibliographies. The edge
width reflects the number of shared references that appear in both papers. The
largest circle contains the 30 largest connected components in the network.
The five smaller circles zoom into the clusters containing papers that include
a suspiciously high number of citations to certain authors. Details regarding
these authors are listed next to each circle.

## Discussion

In this study, we reveal a previously undocumented form of citation
manipulation, namely, the act of purchasing citations. This act is
fundamentally different from previously studied forms of citation
manipulation, such as self-citations (?, ?) and citation cartels (?, ?, ?, ?).
In particular, these forms are defined by, and are hence detectable from, the
topology of the citation network. In contrast, whether or not a citation has
been purchased is exogenous to the citation network, making it impossible to
determine with absolute certainty that a given citation is purchased just by
looking at the topology.

Moreover, the levels of citation manipulation reported in our study push the
limits of what was previously documented in the literature. For example, we
discovered an extremely short paper consisting of just three pages of double-
spaced text, yet has a bibliography consisting of over 200 references, 167 of
which cite the works of the same author. In another case, we identified a
scientist who received exactly 45 citations from 45 different papers,
resulting in a total of 2,025 citations, all received in the same year. Note
that the scientists being cited in both cases are not listed among the authors
of the suspicious papers citing them. As such, this form of manipulation would
be completely overlooked by methods designed to detect self-citations.

Our findings highlight problems with academic databases in general, and Google
Scholar in particular. They also question the practice of considering citation
count when evaluating candidates for recruitment and promotion purposes.
Starting with Google Scholar, we highlight two issues. Firstly, while it
specifies the citations received by a given paper, it does not specify the
citations emanating from a given paper. This makes it extremely challenging to
detect the forms of citation manipulation unraveled in our study. For example,
starting from the Google Scholar profile of the scientist who is cited 167
times in a single paper $p$, one cannot discover this fact without collecting
and analyzing all the citations received by that scientist from their 167
papers cited in $p$. Consequently, those who engage in such questionable
practices can easily go unnoticed by the casual observer browsing Google
Scholar profiles. The second issue with Google Scholar is its complete lack of
moderation when indexing research articles, despite repeated criticism (?, ?).
To this day, Google Scholar continues to index any document it comes across
online, as demonstrated by the fictional Google Scholar profile that we have
created. What makes the above issues more problematic is the fact that, when
it comes to evaluating candidates for hiring or promotion at highly ranked
universities, Google Scholar is more frequently used than all other databases
combined, as our survey has demonstrated. By doing so, we challenge previous
work which suggests that Google Scholar would not become “a main source of
citation analyses especially for research evaluation purposes” (?). The
popularity of Google Scholar highlights the urgent need to rectify the
aforementioned problems.

The citation purchasing industry that we have uncovered is closely related to
other forms of academic misconduct, such as predatory publishing and paper
mills. For example, four out of five papers that supply the purchased
citations were all published in the same “Special Issue” of a journal which
primarily publishes work related to Chemistry. However, the papers authored by
the fictional author were on the topic of “fake news”, and were cited by
papers discussing political issues surrounding fake news. Additionally, the
same special issue published papers related to a wide array of topics
including family policy, Islamic psychology, and education, which are entirely
unrelated to Chemistry. Indeed, publishing a huge amount of “special issues”
that undergoes minimal, if any, peer-review, is a well-known strategy of
predatory publishers to generate profit (?). Furthermore, these four papers
appeared consecutively within the special issue, providing further evidence
that this form of misconduct may not be isolated to the individual authors
that wrote these papers, but may be related to unethical publishing practice
at the journal level. The emergence of profit-oriented predatory publishers
with essentially nonexistent peer review also enabled the proliferation of
paper mills which mass-produce and sell low-quality or plagiarized papers.
While paper mills constitute a relatively well-known form of what is called
“industrialized cheating” (?), we contribute to scientists’ understanding of
such practices by documenting the existence of “citation mills” for the first
time. Taken together, unethical publishing practices such as predatory
publishing, paper mills, and citation mills not only challenge the reliability
of scientometric indicators, but also threaten the integrity of scientific
research.

Unfortunately, the problem of citation manipulation is likely to exacerbate in
the age of generative Artificial Intelligence (AI). Using this technology, a
scientist can easily generate a “scientific” paper, and then plant citations
as they see fit in the bibliography, e.g., to themselves or to another
scientist in return for a fee. As has been shown, current tools are already
capable of producing abstracts that can fool scientists (?), suggesting that
it is only a matter of time until such tools can generate fake, yet seemingly
realistic, papers. In our experiment, for example, the papers that were
generated using AI lack any scientific contribution, yet they were published
on major pre-print servers with little to no push-back. Those who intend to
game the system could also utilize AI to paraphrase existing papers, relying
on their structure and contributions to produce a convincing manuscript.
Indeed, several plagiarism cases have been recently exposed (?, ?, ?, ?), and
the use of AI could only exacerbate this phenomenon.

To remedy the issue of citation manipulation, actions should be taken by both
bibliographic databases and those engaging in scientist evaluation.
Bibliographic databases can report additional metrics designed specifically to
track how citations are accumulated by any given scientist. Past research has
advocated for reporting scientists’ self-citation indices, e.g., the $s$-index
(?), in order to quantify the extent to which a scientist self-cites. In this
study, we proposed the citation concentration index, or the $c^{2}$-index, as
well as the adjusted $c^{2}$-index, to quantify the degree to which a
scientist receives a large number of citations from a disproportionately small
number of sources. As we have shown, a scientist in our dataset received a
$c^{2}$-index of 70, indicating that there exist 70 different papers which
cite that scientist at least 70 times each. Publishing such an index would
flag suspicious profiles such as this one, and may act as a deterrent from
engaging excessively in the practice of planting or purchasing citations.
Furthermore, given that citations play a role in a university’s position in
all major university ranking outlets (?, ?, ?), the motivation behind engaging
in citation and publication manipulation practices may stem from a
departmental or university level push towards increased faculty productivity,
as has been documented in a number of recent cases (?, ?). Bibliographic
databases could also publish information regarding the sources of citations
made to a given author or publication. For instance, one possibility is to
allow users to filter for the quality of the venue from which citations are
received, e.g., whether it is peer-reviewed, has an impact factor greater than
a given threshold, or is published in a Q1 journal, etc. Access to such
information may allow for a more holistic view on a scientist’s citation
profile during the research evaluation process. Future work may examine other
such indices which capture anomalous trends in citation acquisition and paper
authorship, and test their effectiveness in the field.

More broadly, our findings highlight the need for evaluation committees to be
aware of the different ways in which citations can be distorted. While
citation metrics can serve as a practical proxy of scientific success, current
bibliometric databases such as Google Scholar, Scopus, and Web of Science only
offer a superficial perspective into the structure of a scientist’s citation
and publication history. Such a perspective would miss long-lasting
disparities in how submissions, publications, and careers are evaluated. For
instance, scientists who reside in central positions in science due to their
affiliation (?, ?), gender (?), and past achievement (?) tend to fortify their
status due to their incumbent advantage. Moreover, studies have shown that
papers receive more favourable peer-reviews when associated with a well-known
author (?). After being published, those papers again accumulate citations
faster than those authored by less prominent scientists (?). Furthermore,
studies have shown the adverse effect of low citation count of a particular
article on its perceived quality, and in turn, the level of scrutiny in which
it is evaluated (?). Given that citations to a paper can be easily manipulated
through a variety of means, without an awareness of the sources of such
citations, evaluators may unconsciously overestimate the quality of articles
they evaluate. Taken as a whole, our findings bring to light new forms of
citation manipulation, and emphasize the need to look beyond citation counts.

## Methods

### Survey

We reached out to over 30,000 members of the top 10 ranked universities around
the world through email between May 12 and May 23, 2023 to gauge the extent to
which academics consider citations when evaluating other scientists for hiring
and promotion purposes. These universities are: Yale University, Stanford
University, University of Oxford, University of Cambridge, University of
California Berkeley, Massachusetts Institute of Technology, Imperial College
London, Harvard University, Princeton University, and California Institute of
Technology. We collected email addresses from the list of all faculties found
on the website of mentioned universities. At the time of receiving our emails,
the primary affiliation of 38 respondents are with another university not in
the list; see Supplementary Table 1 for the number of respondents from each
affiliation.

Respondents were asked to identify with one of 20 disciplines (in addition to
“Other”) which are then classified into four categories: Natural Sciences,
Social Sciences, Computational Sciences, and Humanities. Natural Sciences
include: Biology, Chemistry, Medicine, Geology, Physics, Environmental
Science, and Materials Science. Social Sciences include: Geography,
Psychology, Political Science, Sociology, Economics, and Business.
Computational Sciences include: Mathematics, Computer science, and
Engineering. Humanities include: Art, History, Philosophy, and
Anthropology/Archaeology. The number of respondents from each discipline is
shown in Supplementary Table 2.

Out of the 30,000 email addresses that we contacted, we received 574 responses
in total. Respondents participate in the survey to answer questions regarding
how they and their colleagues evaluate other scientists; see supplementary
text for the survey questions. Throughout the survey, we do not provide any
information related to the current study that might influence the respondents’
opinion in any way. Furthermore, the participants are presented choice options
in randomized orders so that their choices are not influenced by the
arbitrariness of the ordering.

### Suspicious citation analysis

First, we collected Google Scholar profiles of over 1.6 million unique authors
on the platform. To do so, we performed a breadth-first search to a depth of
10, starting from the profiles of the two corresponding authors of this study.
At each step, all authors listed in the co-authors section of their profile
are collected. For each author, we collected details including their name,
affiliation, research interests, number of citations in each year, and a list
of their publications. Details on the number of authors at each collection
depth can be seen in Supplementary Table 3. Altogether, these scientists come
from 153 countries around the world (Supplementary Figure 5), and span 8 major
disciplines of scientific research including Artificial Intelligence, Biology,
Ecology, Economics, Human-Computer Interaction, Mathematics, Medicine, and
Physics (Supplementary Figure 6).

Next, we narrow the list of authors to investigate by applying a filter to
capture those with significant spikes in their citation history. To this end,
we filter those authors which meet the following criteria; They have at least
(1) 200 citations, to disregard those with low citation counts; (2) 10
publications, to disregard early career researchers; (3) a year in which they
experienced a 10x increase in citations year over year, where this jump
accounted for at least 25% of their total citations, to capture those with
significant citation spikes. This filter resulted in a set of 1016 unique
authors. For each of the resulting authors, we collect all of the citations
they have received over the duration of their academic career.

To identify the outliers, for any given paper c, we count the number of unique
papers p written by an author a and cited by c, which we will denote as n, or
the number of citations received by an author from c. Of the 1016 authors, the
95th percentile of n falls at 3, indicating that in the vast majority of
cases, a given paper will only cite a given author not more than a few times.
To qualify as potentially suspicious, we manually investigate the authors who
have received citations from papers which exhibit an $n\geq 18$, which is the
99.9th percentile in our dataset, resulting in a set of 114 authors. This
threshold is in line with results seen from previous work on citation trends,
which similarly find that 17 citations to a given author from a single paper
was the 99.975th percentile (?). We then select five of the worst offenders
with regards to the percentage of their total citations stemming from papers c
that cite them more than 18 times, where paper c was not authored by the
offender. Here, we purposefully distinguish between self citations and non-
self citations. While self citations have been a subject of research in the
past (14, 15), non-self citations, possibly obtained through illicit means,
have not been studied. To better highlight differences in the nature of the
citations received by the five offenders, we find the authors which matches
them most closely based on a number of factors, including: (1) Academic birth
year, or year of when their first paper was published; (2) their total number
of publications; (3) their total number of citations; and (4) they share at
least one keyword. For each of the five offenders and their respective
matches, we collect the 10 papers c with the largest n value, which we call
their “citing papers”.

### Purchasing citations

After we contacted the email address listed on the website offering the so-
called “H-index & Citations Booster” service (Supplementary Figure 3A), we
were put in touch with the vendor via WhatsApp. According to the vendor,
citations are sold in bulk for either 300 USD per 50 citations or 500 USD per
100 citations; see Supplementary Figure 3B for a screenshot of the
conversation with the vendor where they list the “packages” on offer.
According to the vendor, all purchased citations would emanate from papers
published in one or more of 22 peer-reviewed journals listed on their website,
14 of which are Scopus-indexed. Some of these journals are published by
publishers as recognized as Springer and Elsevier, and have impact factors as
high as 4.79.

Adopting the persona of the fictional author created in the previous section,
we purchased the 50-citations package for the price of 300 USD. Next, we
provided 10 of the 20 papers authored by the fictional profile. Once the
payment was complete, the company quoted a maximum duration of 45 days to
provide the citations. In our conversations with the company through this
process, they declined to elaborate on the exact journals from which the
citations would be emanating from, as well as the number of papers that would
be providing the citations.

Eventually, we identified five papers that supply the purchased citations.
Four of these citing papers were published and indexed on Google Scholar only
33 days after the purchase was made, while the last citing paper was published
after 40 days.

### Identifying clusters of papers based on their co-citation

After identifying the journal from which our purchased citations originate, we
hypothesize that this journal likely contains more citations that were sold in
bulk. To understand whether this is the case, we first collected all papers
published therein in the first half of 2023. Then, we construct a network
where each node in the network represents a paper, and two papers are
connected with an edge if they share at least one identical reference. To
account for typos and formatting errors, we considered two reference items as
identical if their Levenshtein distance similarity ratio exceeded 0.98. As a
result, several connected components emerged in this network of papers. In
each connected component, we scan reference lists for authors appearing at
least 10 times across multiple papers. These authors were identified as
potential candidates for having purchased citations. Then, we manually confirm
whether the set of referenced papers was consistent among all citing papers.
Such consistency is highly improbable by random chance but likely when
citations are acquired in bulk. Our investigation unveiled a total of 11
suspicious authors distributed across five distinct connected components of
papers. These authors received citations of at least 10 times per paper from
multiple sources.

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## Acknowledgments

The support and resources from the High Performance Computing Center at New
York University Abu Dhabi are gratefully acknowledged. ChatGPT was used during
the writing process to improve readability.

### Ethics

The research was approved by the Institutional Review Board of New York
University Abu Dhabi (HRPP-2023-5). All research was performed in accordance
with relevant guidelines and regulations.

### Funding

H.I. and F.L. are supported by the New York University Abu Dhabi Global Ph.D.
Student Fellowship.

### Authors contributions

T.R. and Y.Z. conceived the study and designed the research; H.I. collected
the data; H.I. and F.L analyzed the data; H.I., F.L, T.R., and Y.Z. wrote the
manuscript.

### Competing interests

The authors declare no competing interests.

### Data and materials availability

Aggregated or anonymized data to reproduce the figures will be shared upon
publication. To avoid publicly shaming the authors, editors, and journals
whose suspicious behaviour is documented in our study, the original dataset
will not be shared with the public. We are, however, willing to share the
original dataset with the editors and reviewers for them to confirm the
validity of our findings.

## Supplementary materials

Supplementary Note 1
Supplementary Figure 1 to 6
Supplementary Table 1 to 3
Appendices S1 to S2