The Role of Fake Review Detection in Managing Online Corporate
R. E. Loke
and Z. Kisoen
Centre for Market Insights, Amsterdam University of Applied Sciences, Amsterdam, The Netherlands
Keywords: Online Reviews, Feature Extraction, Spam Detection, Supervised Machine Learning, Strong Corporate
Reputation Management.
Abstract: In a recent official statement, Google highlighted the negative effects of fake reviews on review websites and
specifically requested companies not to buy and users not to accept payments to provide fake reviews (Google,
2019). Also, governmental authorities started acting against organisations that show to have a high number
of fake reviews on their apps (DigitalTrends, 2018; Gov UK, 2020; ACM, 2017). However, while the
phenomenon of fake reviews is well-known in industries as online journalism and business and travel portals,
it remains a difficult challenge in software engineering (Martens & Maalej, 2019). Fake reviews threaten the
reputation of an organisation and lead to a disvalued source to determine the public opinion about brands.
Negative fake reviews can lead to confusion for customers and a loss of sales. Positive fake reviews might
also lead to wrong insights about real users’ needs and requirements. Although fake reviews have been studied
for a while now, there are only a limited number of spam detection models available for companies to protect
their corporate reputation. Especially in times with the coronavirus, organisations need to put extra focus on
online presence and limit the amount of negative input that affects their competitive position which can even
lead to business loss. Given state-of-the-art derived features that can be engineered from review texts, a spam
detector based on supervised machine learning is derived in an experiment that performs quite well on the
well-known Amazon Mechanical Turk dataset.
The last few months have changed the landscape of
the world drastically (McGrath & Ross, 2020). The
outbreak of COVID-19, or the coronavirus, is already
stamped as a human tragedy and has a growing
impact on the global economy. To sustain, especially
the business industry is facing a huge number of
challenges to cope with (Gerdeman, 2020). Iansiti et
al. (2020) state that business leaders all over the world
are struggling with a wide variety of problems from
decreasing sales and stalling supply chains to keeping
employees safe and ensure that the operational core
can continue operating without too many obstacles
from the coronavirus. Another recently published
study from McKinsey (2020) shows that although the
coronavirus has caused the biggest quarterly drops of
shares since 1987, a record of unemployment claims
and a crude drop of oil prices globally, it has turned
more people to technology than ever. Governments
around the world have urged people to work from
home where possible, this together with the lockdown
measures leads to a new way of using technologies in
our daily lives. According to the Dutch Institute of
International Relations (2020), “COVID-19 is a
digital pandemic in terms of its origin, and it is also
one in its effects”. As workplaces instruct employees
to work from home, universities shift fully to online
teaching and the restaurant industry transitions faster
than before to online ordering and delivering; one of
the most rapid organizational transformations in the
history of the modern firm is happening right now
(Iansiti & Richards, 2020). In this huge digital
transformation, organizations are forced to move to a
fundamentally new operating architecture based on
software, data, and digital networks. With more
digitally at stake for organisations, the online
corporate reputation has become more important than
ever and can mean the deal breaker between surviving
in times with the coronavirus or not.
Loke, R. and Kisoen, Z.
The Role of Fake Review Detection in Managing Online Corporate Reputation.
DOI: 10.5220/0011144600003269
In Proceedings of the 11th International Conference on Data Science, Technology and Applications (DATA 2022), pages 245-256
ISBN: 978-989-758-583-8; ISSN: 2184-285X
2022 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
According to Chandler (2020), the coronavirus
has driven a massive rise in the use of technology
globally. In their recently published article it is stated
that “the coronavirus boosted online spending and
usage in Q1 of 2020 to the highest in history”. It also
shows that digital platforms are thriving as consumers
seek more entertainment, shopping opportunities and
new ways of connecting during the crisis. This
increase of online behavior generates more data for
organizations to work with improving their online
corporate reputation. More organisations start to
realize the importance of having an online strategy
and strong digital visibility as part of corporate
reputation. In times of the corona pandemic,
organisations rely more than ever on strong online
presence in terms of their websites and apps (Lincoln,
2020). Recent research from The New York Times
(2020) stated that people are spending almost one
hour a day extra on websites since the outbreak of
COVID-19. This means that an important way to
reach a broader audience is by having a multi-channel
strategy including an app, social media pages and
websites. However, with more organisations
strengthening their digital strategies; the online
market becomes more crowded in terms of
competitors. Also, organisations that shift from a
traditional marketing toolbox to multi-channel
become more vulnerable in terms of corporate
reputation. The rise of social media and reviewing
websites has empowered consumers and weakened
the position of organisations by exposing them to
negative publicity, customer attacks and reputation
damage (Horn, Taros & Dirkes, 2015). In order to
provide a very actual and up-to-date research, this
study will focus on the rising concern of fake reviews
and its relationship with corporate reputation.
Fake reviews can quite easily be written by
anyone on the Internet. Martens and Maalej (2019)
state that reviews as a feedback form is often used by
managers to prepare organisations for business
decisions and to measure corporate reputation of
organisations. Research shows that positive feedback
improves app downloads, sales and the reputation of
the company. However, as a side effect, a market for
fake reviews has emerged which can turn into very
negative consequences for organisations (Martens &
Maalej, 2019). For several years now, there has been
done extensive research on the effects of negative and
fake reviews on online corporate reputation. Many
researchers indicate that small insignificant
comments or reviews can have a far-reaching impact
on an organisation (DiMauro & Bulmer, 2014).
According to Otar (2018), negative and fake reviews
can damage corporate reputation online and business
growth. Stats show that only four negative or fake
reviews can cost an organisation 70% of potential
customers (Otar, 2018). Especially fake reviews are
recognized as a real challenge by both the research
community and the e-commerce industry. As many
giant app stores as Google and Apple try to combat
against fake reviews, almost 15-30% of all reviews
are estimated to be fake per product or service
(Barbado et al., 2019). Therefore, fake reviews in app
stores can be seen as an actual, critical business
problem that affects all layers of businesses.
Fake review detection has been a hot topic in
research and industry for many years now (Li, Lui &
Qin, 2018). However, it remains interesting to
analyse the background and effect of fake reviews in
business and, because of a generally noted low
accuracy of detecting fake reviews by people, how
these can be detected using machine learning
methods. With the rising market for apps,
organisations have become more vulnerable to user
feedback in form of app ratings and reviews. As
research shows that even a single fake review can
have a significant impact on business, it will be
important to take this problem seriously and analyse
it below in a survey in more detail. In addition, the
outcomes of an experiment that was conducted are
reported on below and made available for other
companies in order to tackle the issue of fake app
The remainder of the paper below has been
logically structured into sections on literature review
(Section 2), research methodology (Section 3), results
(Section 4), discusion (Section 5), and conclusions
(Section 6).
In this section, we first give an in-depth description
and background of corporate reputation and then
explain and discuss on developments in the
phenomenon of fake reviews that can be related to
online corporate reputation. Thereafter, we stipulate a
preliminary conceptual model and report on common
spam review detection techniques.
The aim is to provide an overview of current
state-of-the-art knowledge addressing relevant
theories, methods, and unforeseen gaps in existing
2.1 Corporate Reputation
The definition of corporate reputation has been
widely discussed over the years in the research
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
industry and is in continual change. Although it is a
hot topic, this concept is still vague and has many
different definitions that sometimes even contradict
each other. According to Giovanni (2010, p.74), “the
reputation of a company can be considered one of the
most valued organizational assets”. Chun (2005) and
Dowling (2016) both agree that corporate reputation
has one aligning element; the term is often described
as a reflection of the company to insiders and
outsiders. Also, corporate reputation is often linked
with terms as corporate identity, corporate image, and
corporate goodwill (Wartick, 2002; Barnett et al.,
For this study, it will be important to set one
straight direction for corporate reputation; therefore,
the definition from Fombrun and van Riel (1997) will
be maintained throughout the paper. According to
their early days research corporate reputation can be
identified as “a perceptual representation of a
company’s past actions and future prospects that
describes the firm’s overall appeal to all of its key
constituents when compared with other leading
rivals”. Another important finding that comes across
in most academic papers on corporate reputation is
that many researchers define corporate reputation as
a collective concept; it is seen as the sum of the
perception of external stakeholders (Barbado et al.,
2019; Barnett et al., 2006; Horn et al., 2015). Chun
(2005) states that corporate reputation can be seen as
an umbrella construct for corporate image and
corporate identity.
Figure 1: Key elements of corporate reputation (Chun,
Figure 1 shows the statement from Chun (2005)
that identity, desired identity, and image are partly
independent variables that form corporate reputation.
Image can be described as the perception of others of
a company or how it is formulated “how others see
us” (Chun, 2005). Identity can be described as an
internal view of the company what means how
members of the organization perceive, feel, and think
about the company (“how we see ourselves”).
Desired identity describes how an organisation wants
to be perceived which refers to the name, logo,
symbol as well as strategic actions and philosophy
(“how we want others to perceive ourselves”). The
gap in the middle represents how an organisation is
being perceived internally and externally, as well as
how it wants to be perceived (Chun, 2005). A wide
gap indicates inconsistencies in strategy or
communication and can damage the corporate
reputation of an organisation. Walker (2010) states
that alignment between these variables can lead to
strategic benefits, such as increasing profitability,
lower costs, and a competitive advantage.
We will now discuss on the relevant concepts of
electronic worth-of-mouth (EWOM), online
corporate reputation and corporate reputation
Table 1: Touch points of EWOM (adapted from Mishra &
Satish, 2016).
Stage Touch points of EWOM
Problem or interest External stimuli (ads on
websites, social media
personalization and
Information search Search engines, social media,
product websites, e-retailers
Evaluation of
Websites with compare
options, social media for
feedback, online reviews, and
rating websites
Purchase decision Channels (e-commerce
websites), discussion and
feedback on social media
Review sites, social media,
online rating and reviews,
feedback on social media or
product sites
2.1.1 EWOM (Electronic Word-of-Mouth)
Internet and social media platforms have added a new
element to the traditional word-of-mouth (WOM)
term. Electronic word-of-mouth, or EWOM, refers to
any positive or negative content made by potential,
actual, or previous customers about a product or
company, which is made available to an audience of
people and institutions via the web 2.0 (Mishra &
Satish, 2016). EWOM is expressed in different forms
of communication such as opinions, online ratings,
online feedback, reviews, comments, and experience
sharing via online communication channels.
According to a study from Mishra & Satish (2016) on
EWOM, it plays a critical factor in marketing efforts
The Role of Fake Review Detection in Managing Online Corporate Reputation
and has an impact on different stages in the consumer
purchase decision process. Table 1 shows how
consumers are in touch with EWOM during the
purchasing process (Mishra & Satish, 2016; Dewey,
Although there seems to be a clear link between
EWOM and corporate reputation, there is little
literature on this connection. Hoyer and Macinnis
(2001) found out that WOM is the most credible and
objective influence on corporate reputation. Other
researchers agree that in meeting or exceeding
customers’ expectations, customer satisfaction is
achieved, EWOM is uttered, and good reputations are
built (Davies et al. 2010). However, the corporate
reputation of companies is considered fragile; while
it may take time to build, it can be easily destroyed.
2.1.2 Online Corporate Reputation
A concept that often simultaneously appears with
corporate reputation is online reputation. According
to Jones, Temperley and Lima (2010), “online
corporate reputation is a reputation, which involves a
corporate reputation created in the online
environment”. Online reputation is not only created
on social media but is also created by groups of
people sharing and collaborating online and through
search engines as Google, Ask and Yahoo (Weber
Shandwick & KRC Research, 2019). In this digital
era, online corporate reputation is as important as
offline reputation (Abimbola & Vallaster, 2009). The
emergence of social media platforms and review
websites allows people to have new tools to
publically judge companies at a much greater and
faster pace than before. On these platforms,
consumers do not only discuss content from
companies, but they also create it (Barnett et al.,
2006). Fournier and Avery (2011) have defined social
media as “a venue for open-source branding” in
which consumers can co-create the nature of
reputations of a brand. Companies try to influence
this process of co-creation by creating solid online
presence and strong online marketing strategies. The
online presence, according to Waters et al. (2009),
“offers various benefits to companies like the
opportunity to communicate directly with customers,
strengthen relationships, stimulate co-creation and to
assess consumer’s brand attitudes”. Nowadays,
companies experience more pressure from outside to
take part in online conversations that influence
corporate reputation. Therefore, the online corporate
reputation is associated with increased loss of control
and increased need for active monitoring (Gensler et
al., 2015).
2.1.3 Corporate Reputation Management
Since the overall goal of this research is to contribute
to a good online reputation management for
companies (for example, by emphasizing genuine
reviews in EWOM to consumers and eliminating fake
ones), it is important to understand the meaning
behind reputation management.
According to Hutton et al. (2001), reputation
management, which is considered a business
function, is based on the traditional term “public
relations”, or also known as “corporate affairs”. Beal
& Strauss (2009) state that online reputation
management is placed between marketing
communications, public relations, and search engine
optimization (SEO). Jones et al. (2010) agree with
this definition as they list: “online reputation
management is the process of positioning,
monitoring, measuring, talking and listening as the
organization engages in a transparent and ethical
dialogue with its various online stakeholders”. What
comes across from different literature is that to build
and maintain corporate reputation, it is important for
a company to understand who its stakeholders are and
how they perceive the company (Beal & Strauss,
2009). This can be linked to the umbrella theory of
Chun (2005) and is aligned with the perception that
reputation is formed by a collective perception of
different individuals. The more the perceptions of
several individuals are aligned with each other, the
stronger the corporate reputation of a company
(Gensler et al., 2015).
When looking at how corporate reputation can
best be maintained, research from Page and Fearn
(2005) indicates that organisations should focus on
aligning the perceptions of different stakeholders. To
do so, organisations should focus on clear
communication about leadership and successes of the
organisation and the organisation’s perspective on
consumer fairness in advertisement, marketing,
websites, reviews, and other forms of
communication. To go more in-depth on this: the
reputation of an organisation is reflected by the
leadership style and its successes from the CEO. A
clear example of this is Tesla, an automotive
company that is mainly known for its famous CEO,
Elon Musk. The reputation is also reflected by
consumer fairness including the fair treatment of
consumers regarding pricing, quality of products and
services and transparency in advertisement which
also includes reviews.
To conclude on reputation management, literature
indicates that it is important for organisations to
measure, monitor and co-ordinate the different
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
stakeholder reputations with the overall goal to align
these as much as possible. Page and Fearn (2005) and
(Gensler et al., 2015) emphasize strongly that the
more different stakeholder reputations are similar, the
stronger the corporate reputation of an organisation
is. To create alignment, organisations should focus on
creating clear and transparent messages with regards
to leadership style, successes of an organisation,
advertisement and marketing communication. It is
important for an organisation to be authentic and
transparent towards all its stakeholders.
2.2 The Role of Fake Reviews in
Corporate Reputation
In today’s tech-savvy world, review websites, social
media and mobile applications have become the most
important source for consumers to express
themselves. It is considered very easy for people to
share their views about products and services using e-
commerce websites as TripAdvisor and Trustpilot,
forums and blogs (Hussain, Mirza, Rasool, Hussain
& Kaleem, 2019). In app stores in particularly, users
can rate downloaded apps on a scale from 1 to 5 stars
and write a review message in which they can express
satisfaction, report bugs, or make suggestions
(Martens & Maalej, 2019). A recent study on online
consumer buying behaviour confirms the statement
that most people read these reviews about products
and services before buying them (Xhema, 2019). In
case of apps, consumers often read through the
reviews before deciding to download the app.
Harman, Jia, and Zhang (2012) identified in their
research that there is a positive relationship between
the number of positive ratings and reviews to sales
and download ranks of apps. As is stated, “stable
numerous ratings lead to higher downloads and sales
numbers”, which will have a positive effect on
corporate reputation (Barnett et al., 2006).
As a result of the positive connection between
reviews and sales, a new illegal market that is focused
on producing fake reviews has emerged. The
phenomenon of producing fake reviews on products
and services with the goal to boost sales is also
referred to in academic studies as “spam attack”
(Hussain et al., 2019). In regular situations, real users
are motivated by their satisfaction level to provide
feedback on apps; however, fake reviewers get paid
or similarly rewarded to submit reviews (Martens &
Maalej, 2019). An important distinction between real
users and fake reviewers is that fake reviewers might
not even be real app users and thus their reviews
might not be truly reflecting honest opinions.
According to Martens and Maalej (2019), fake
reviews can be defined as non-spontaneous, requested
and rewarded. Another definition states that a fake
review is a positive, neutral, or negative review that
is not an actual consumer’s honest and impartial
opinion or that does not reflect a consumer’s genuine
experience of a product, service, or business
(Fontanarava et al., 2017).
Many studies agree that fake reviews have a
negative effect on the online corporate reputation of a
company (Horn et al., 2015; Barbado et al., 2019;
Xhema, 2019; Hussain et al., 2020). One of the main
issues with opinion sharing websites and apps is that
fake reviews can easily create hype about a particular
product based on misleading information. These fake
reviews can become the key factor for consumers in
their buying decision and thus lead to negative
financial consequences. Although it seems clear for
people that not everything on the Internet is
believable, research shows that almost 84% of
consumers consider online reviews to be as
trustworthy as personal recommendations. However,
for organisations to make use of fake reviews or to
have fake reviewers can harm the corporate
reputation by creating false expectations. Also, true
reviews can help organisations learn where to
improve and can be beneficial in increasing success
for business. Secondly, if an organisation gets caught
buying fake reviews for its own products or for
decreasing the value of those of its competitors, it will
lead to much more reputation loss than it possibly
would gain. An example from 2013 is Samsung
which was fined for paying people to negatively
review HTC products. Another example is a report
from BBC that showed that fake online reviews get
openly bought and sold and that shoppers often can
get products for free in return for fake reviews.
We now first underline why it can be extremely
important for a company to focus on strong corporate
reputation management and then elaborate on the role
of fake reviews in consumer buying behavior that can
be related to corporate reputation.
2.2.1 Benefits of Strong Corporate
Reputation Management
The above-mentioned examples indicate what can
happen if organisations do not put effort in strong
reputation management and alignment of stakeholder
reputations as discussed by Page and Fearn (2005).
Positive reputation can strengthen the overall
performance of an organisation, while negative
reputation is considered a competitive disadvantage
(Aula, 2010).
The Role of Fake Review Detection in Managing Online Corporate Reputation
According to Helm and Klode (2007), there are
five major benefits that strong corporate reputation
can bring to an organisation. These are as follows: (1)
Increased financial performance; (2) Greater
competiveness; (3) Higher satisfaction and loyalty
among consumers; (4) Attract and retain employees;
(5) Support in crisis. Some explaining notes on this:
Firstly, the first benefit logically can result in an
increased stock value. According to Helm and Klode
(2007), a strong reputation limits risks for investors,
who are more willing to spend money on the
organisation. Secondly, the second benefit goes hand-
in-hand with increased financial performance. Helm
and Klode (2007) identify that organisations with
strong corporate reputation can easily charge higher
prices due to the fact that consumers perceive the
quality of products and services as better. Thirdly,
several studies indicate that a good corporate
reputation can increase benefit number (3) (Helm and
Klode, 2007; Chun, 2005; van Riel & Fountain;
2008). Fourthly, a positive company image attracts
more highly skilled employees, hence, benefit
number (4) (Helm & Klode, 2007). Lastly, according
to Helm and Klode (2007), in times of crisis for an
organisation, a positive reputation can help
companies to overcome economic consequences.
Organisations with a strong image experience less
market decline compared to organisations with a
weak reputation (van Riel & Fombrun, 2008).
To conclude, strong corporate reputation can
bring several major benefits to an organisation. These
benefits are linked to financial, strategic, and
competitive advantages that all have a positive effect
on the performance of an organisation. Therefore, it
is highly advisable and important for an organisation
to focus on strengthening its corporate reputation and
on limiting threats as fake reviews.
2.2.2 Fake Reviews in Consumer Buying
A study from Constantinides and Fountain (2008)
describes relationships when consumers are exposed
to information about organisations. There are four
identified stimulating factors, A, B, C and D, see
Figure 2, that each affect the purchasing decision.
Although purchasing behaviour should be threatened
separately from corporate reputation, it is important
to describe the theory from Constantinides and
Fountain (2008) in order to emphasize the role that
fake reviews play in purchasing behaviour.
Organisations that use fake reviews, attempt to make
from stimuli D a controllable stimulating factor.
Since Constantinides and Fountain (2008) postulate
that all stimulating factors are equally distributed, this
explains why organisations with bad reputations, as
part of their sales strategies, focus on making the
uncontrollable controllable (Grutzmacher, 2011).
Figure 2: Four stimuli on consumer behaviour
(Constantinides and Fountain, 2008).
2.3 Conceptual Model
The goal of the conceptual model that we postulate is
to visualize the concepts in this study and indicate the
modeling playground between fake reviews and
corporate reputation. The model, inspired by
Fombrun (1997), shows how several variables that
have been identified in the above literature frame are
related to each other and eventually create corporate
reputation; see Figure 3.
According to Dowling (2016), firstly, corporate
identity is, in short, how people recognize an
organisation. Secondly, corporate image is defined as
“a set of beliefs and feelings an audience has about an
organization”. This all leads to corporate reputation,
that is formed by the judgement about the
organisation’s attributes as is indicated in the
conceptual model.
Figure 3: Fake reviews and corporate reputation: a
conceptual framework that we propose in this paper that has
been derived from scientific literature (see text in 2.3).
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
We stipulate that (fake) reviews play an important
role on the perception of the customer and community
2.4 Spam Review Detection
As fake reviews are becoming much more of a
problem with more review websites popping up and
with consumers’ ability to produce feedback at any
time, demand for spam detection methods is rising.
As we have discussed, such methods are needed for
strong corporate reputation management. However,
as much more research has recently appeared on the
topic of spam detection, the practical implication
seems to remain a challenge. Major review websites
as Yelp and Amazon have already taken first steps in
detection of fake reviews on their websites; however
there seems to be a lot of room for improvement. For
instance, Hussain et al. (2019) researched several
spam detection techniques. According to their paper,
spam detection consists out of the following steps: (1)
Gather a review dataset; (2) Select feature
engineering; (3) Apply, for example, machine
learning techniques. Below, each of these three steps
will be separately discussed in depth in order to
generate useful findings for implementing a spam
detection model in the experiment that we set up.
2.4.1 Gathering a Review Dataset
To be able to set up a machine learning model for
review spam detection, it is important to have a
dataset to work with. However, in terms of spam
detection it is considered difficult to find an available,
labelled dataset (Hussain et al., 2019). A prior
inventory on spam detection models indicates that
there is only one labelled hotel review dataset
available that includes review text and has no other
features available (Kaggle, 2020). Many of the
studies that analyze spam detection methods do not
publish used datasets publicly, which makes it
difficult for new researchers to continue to optimize
and improve on spam detection models. It can be
stated that after researching multiple studies on spam
detection, only a limited number of labelled datasets
are available which is contradicting the high current
urgency for spam detection methods in society.
2.4.2 Feature Engineering
According to Hussain et al. (2019), the linguistic
approach is the most common approach for feature
extraction from review datasets. As they explain in
their research, this approach focuses on review text
and includes data pre-processing, tokenization,
transformation, and feature selection. In the next
section, in Section 3, an experimental setup of how all
these practical steps can be executed for spam
detection will be given; we will now proceed with
discussing step 4 which is the most crucial step
because it has the most significant effect on the
performance of spam detection models. Previous
research on feature selection, according to Hussain et
al. (2019), shows that the following spammer features
are used to detect spam and non-spam reviews: (1)
Maximum number of reviews: previous research
indicates that spammers write often more than one
review per day. (2) Percentage of positive reviews:
most spammers write positive and favourable
reviews; therefore, a high percentage of positive
reviews could indicate spam reviews. (3) Review
length: most spammers do not write very lengthy
reviews with a lot of details. Therefore, short reviews
can indicate spam reviews. (4) Reviewer deviation:
spammers give often very high ratings, therefore this
rating deviates from the average review rating. (5)
Maximum content similarity: research shows that
similar reviews are used for multiple products and
services over different organisations.
After analyzing several sources in the study of
Hussain et al. (2019) it shows that the linguistic
approach holds the highest accuracy in terms of spam
detection methods. However, it all depends on the
feature selection process as features become the input
for the actual spam review detection method that
might be in place.
Figure 4: Taxonomy of spam review detection techniques
(Hussain et al., 2019, p. 13).
2.4.3 Machine Learning Techniques
To be able to classify reviews in the two classes of
spam and non-spam, it will be needed to choose the
appropriate classification model. Hussain et al.
(2019) published a taxonomy of spam detection
techniques (Figure 4). It was created to enable other
researchers “to classify existing approaches and to
The Role of Fake Review Detection in Managing Online Corporate Reputation
figure out the most appropriate technique to solve a
spam detection problem”. Spam detection models fall
into two categories (see again, Figure 4): machine-
learning based methods and lexicon-based approach.
The first approach can be classified into supervised
and unsupervised learning. Research shows that the
accuracy of supervised learning in terms of Support
Vector Machine and Naïve Bayes is best; for
unsupervised learning, Aspect Based, and K-Nearest
Neighbour is best. In this paper, the focus will lie on
machine learning techniques, therefore, the Lexicon-
based approach will not be further discussed. For an
overview of accuracy rate per approach, please refer
to Hussain et al. (2019).
We adopted an exploratory research methodology, as
we intended to generate general insights about the
fake review problem that the business industry is
currently dealing with in society. Of course, we held
our main drive that is targeted towards the relevancy
of online corporate reputation for e-commerce in the
back of our minds.
Figure 5 graphically represents our research
methodology that consisted of data collection,
preparation, and analysis processes. Below, we give
some more detail about the datasets that we employed
as well as how we concretely implemented our data
processing and machine learning.
Figure 5: Research design process.
3.1 Datasets
Our main dataset was obtained from the open-source
data platform Kaggle (2020). This is a well-known
hotel reviews dataset from Amazon Mechanical Turk
in combination with TripAdvisor. It has been created
for researchers to provide new solutions on the fake
review issue and to develop and test new spam
detection models. During the production of this
(supervised) dataset, a group of people were paid to
write 400 fake positive and 400 fake negative reviews
about hotel experiences. These deceptive reviews
were added together with 800 genuine, thruthfull
reviews (again, 400 positive and 400 negative). The
total number of reviews is hence 1600. Positive and
negative refer to the sentiment in a review text. The
dataset was obtained in April 2020, via the Kaggle
As a good common practice, the dataset was
descriptively analysed on any differences between the
groups deceptive versus truthful, negative versus
positive, and TripAdvisor versus non-TripAdvisor.
We found no significant statistical difference (p-value
= 0.2) for the length of words in deceptive and truthful
reviews, but significant statistical difference (p-value
= 0) for the length of words in negative and positive
reviews as well as in TripAdvisor and non-
TripAdvisor reviews.
In addition, with the goal to test and apply our
algorithms to other datasets that are relevant for many
businesses, a scraper was built to crawl about 200.000
product reviews of eight food and beverage suppliers
from the review site Google Play. For this scraper, an
algorithm was built in Python by using the package
Google Play Scraper. However, it was too
challenging and too costly to turn this dataset into a
supervised dataset with spam identification labels that
we needed to feed our supervised machine learning
3.2 Data Processing
After understanding the dataset and the structure of
the data, the next step was to process the data.
Non meaningful stop words were removed from
reviews using a natural language toolkit library.
It is important to explain how relevant review text
features were computed. First, we logically computed
the length of words variable that we already have
mentioned in the previous section. Second, we
included the sentiment polarity (positive or negative).
Third, since Ott et al. (2011) state that in review
classification there is a large difference between
informative and imaginative writing, namely that the
former typically consists of more nouns, adjectives,
prepositions, determiners, and coordinating
conjunctions, while the latter includes more verbs,
adverbs, pronouns, and pre-determiners, for each word
in a review, Parts of Speech components were
extracted to be able to feed this as a feature vector in
the machine learning model. Fourth, we experimented
with weighting meaningful words to form topics.
DATA 2022 - 11th International Conference on Data Science, Technology and Applications
All review text data were vectorized using
3.3 Machine Learning
Once relevant features were extracted, it was time to
split the data into a training and test set. We used the
following, common split: 80% training and 20%
testing. Only the training data was used to implement
machine-learning models.
We implemented the following machine learning
models: Logistic Regression, Decision Tree, Support
Vector Machine (SVM), and Random Forrest.
We used GridSearchCv to finetune and find best
hyperparameters for ML algorithms.
We also systematically tested our machine
learning models using Random States.
The machine learning model that did best was
Support Vector Machine; see Table 2 for several of
its model performance scores on the test data set of
320 reviews. The accuracy rate was 89%. When we
systematically tested this model with random states, a
slightly higher accuracy rate could be obtained.
Table 2: Several performance scores of our SVM machine
learning model.
precision recall f1-score support
deceptive 0.88 0.90 0.89 155
truth 0.90 0.88 0.89 165
avg /
0.89 0.89 0.89 320
To check on any welcome generalization
capabilities of the machine learning model, we also
tested the model on several reviews from Yelp that
are likely to be in the same application domain and
the outcomes were promising; see Figure 6.
Figure 6: Generalization capabilities of our SVM machine
learning model on some Yelp reviews.
Table 3: Comparison of several good-scoring spam
detection models, i.e., different supervised learning
techniques on different datasets (adapted from Hussain et
al. (2019)).
When we compare the results of our study to other
related work, see Table 3, we may note that our spam
detection model yields a high performance when
compared to other models. Our best performing
machine learning model would score fourth place in
terms of accuracy in this list, with an accuracy like
that was obtained in the two studies that use the same
dataset that would be on a third and fifth place in
terms of accuracy, respectively. It clearly
outperforms other studies in terms of accuracy and,
since accuracy is one of the most important
performance indicators, could therefore logically
serve and practically be applied in any defence
strategy that an organisation might want to define in
order to be able to tackle fake reviews.
Our study contributes to the research community
by providing another successful example of how fake
reviews can be detected.
Although fake reviews are a rising concern and a
hot topic in the machine learning domain,
unfortunately, not many datasets in which fake
reviews have been identified are accessible.
Obviously, in supervised learning, a large, diverse
dataset is needed for proper training of classifiers in
different application domains.
The scarcity of labelled datasets forms a real
challenge for further research in the field. It can be
recommended to synthesize and produce a new
labelled dataset to bring more variety into the domain
of spam detection. Currently, many models are being
The Role of Fake Review Detection in Managing Online Corporate Reputation
built upon the same sort of data, and, therefore, it will
be valuable for future research to have different or
more ample datasets to analyse.
Many organisations struggle with defending their
online corporate reputations against fake reviews. It
can be argued that positive and neutral fake reviews
have, similar as is the case for negative fake reviews,
negative consequences on corporate reputation. To
provide organisations with an asset for corporate
reputation management, a state-of-the-art machine
learning model has been built that separates fake
reviews from regular ones. The model yields a high
accuracy rate compared to others, and, therefore, it
can be said that this model could be successfully
implemented by organisations as part of their
corporate reputation management strategy. In the
future, the model should be further optimized and
extended to incorporate new datasets that are relevant
for organisations by finetuning the processing steps
that we have inpictured in this paper.
This paper has been inspired on the MSc master
project of Zoë Kisoen who was involved via the
master Digital Driven Business at HvA. Thanks go to
Frederik Situmeang as well as several anonymous
reviewers for providing some useful suggestions to an
initial version of this manuscript. Rob Loke is
assistant professor data science at CMIHvA.
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