# Fraud Review Detection: Methods, Challenges, and Analysis

Saeedreza Shehnepoor*, Roberto Togneri, Wei Liu, Mohammed Bennamoun, S.
Shehnepoor (*corresponding author), R. Togneri, M. Bennamoun, and W. Liu is
with the University of Western Australia, Perth, Australia. emails:
<EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS><EMAIL_ADDRESS>
###### Abstract

Social reviews have dominated the web and become a plausible source of product
information. To gain more customers, businesses can hire a single user, groups
of users, or a bot trained to generate fraudulent content. To provide an
exhaustive survey on fraud review detection, we propose a framework that
categorises the large volume of works in this area using three key components:
the review itself, the user who carries out the review, and the item being
reviewed. The literature is reviewed based on behavioral, text-based features
and their combinations. With this framework, a comprehensive overview of
approaches is presented including supervised, semi-supervised, and
unsupervised learning. The supervised approaches for fraud detection are
introduced and categorized into two sub-categories; classical, and deep
learning. The lack of labeled datasets is identified with potential solutions
suggested. To assist new researchers in understanding the field, a summary of
future directions is included at each stage of the proposed systematic
framework.

###### Index Terms:

Fraud Review, Components, Features, Classification, Deep Learning

##  I Introduction

User generated data in the form of social reviews is one of the defining
features of Web 2.0. This has motivated businesses to not only understand, but
also take advantage of the opportunity presented by social reviews to promote
their products and services, resulting in their own benefit and potentially
the loss of their competitors. This unfortunately also motivates some
businesses to employ fraudsters to spread fraudulent information through
social review platforms such as Amazon and Yelp.

###  I-A Definitions

To facilitate understanding, we will first define the following key concepts:

  * •

Fraud Review: A review intentionally written by fraudsters to promote/demote
services to boost gain/loss of the businesses providing the services.

  * •

Fraudster: A user paid by a business to write fraud positive reviews on their
own business to promote services and products or to write fraud negative
reviews on their competitors’ services and products.

  * •

Fraudster Group: Fraudsters may form a group to collectively promote (demote)
their own (competitors’) services to dominate the sentiment of a service.

  * •

Target Item: The service or product that a fraudulent review is written about.

Figure 1: A systematic framework of steps in a typical fraud detector.

###  I-B Fraud Review in Comparison with Other Types of Spam

Compared with other spam contents (e.g., email spams, insults, threats,
malicious links, and fake news), fraud review detection is more challenging.

Insults and threat detection can rely on sentiment analysis to find abusive
comments [1, 2, 3]. Malicious links are detectable through blacklists [4, 5,
6, 7]. Building blacklists are among the most popular approaches to block
malicious links. The blacklists are provided through several websites such as
jwSpamSpy111http://www.jwspamspy.net, PhishTank222http://www.phishtank.com/,
and DNS-BH333http://www.malwaredomains.com. Such blacklists are built using
user feedbacks and mechanisms to detect malicious URLs.

Fake news refers to articles intentionally written to convey false information
for financial and political purposes. Though knowledge of political science,
journalism, and psychology is needed to detect fake news, such contents are
still detectable through fact-checking techniques as factual truths are
available [8, 7, 9].

Fraud reviews, on the other hand, are written or automatically generated in a
similar way to genuine reviews. Hence, it is reported that even expert human
judges find it hard to ascertain whether a review is fraud or not [10].
Therefore, characterizing and detecting fraud reviews is not as simple as
other types of spams. Additionally, studies [11] show that fraud reviews
increased in Yelp by 5% to 25% from 2005-2016, while a rating increase of
1-star in Yelp may lead to a 5-9% increase in revenue for a restaurant [12].
Hence, fraud detection is important to social review services such as Yelp and
Amazon to provide spam-free platforms for users. As illustrated in Fig. 1, a
social review detector can be viewed as the interaction of three types of
constituents, namely, User, Review and Item. Computationally, a social review
detector can be represented as a set of triplets $\langle$User, Review,
Item$\rangle$, which denotes the fact that a user wrote a review about an
item. To facilitate using a different annotation, their description is given
in Table I.

TABLE I: List of symbols and their corresponding descriptions. Symbol |  Description
---|---
$R_{u}$ |  Review written by user $u$
$X^{i}$ |  $i^{th}$ element of component $X$ (e.g. $R^{i}_{u}$ is $i^{th}$ review of user $u$)
$N_{X_{u}}$ |  Number of $X$ attribute on user $u$
$NR_{u}$ |  Negative review written by user $u$
$PR_{u}$ |  Positive review written by user $u$
$Rating_{u}$ |  Rating given by user $u$
$avg(X_{u})$ |  Average of $X$ attribute on user $u$
$R_{u-p}$ |  Review written from user $u$ on item $p$

###  I-C Fraud Review Categorization and Identification - Past Literature
Surveys

In the research community, recent studies focused on the identification of
fraud reviews and their characteristics. Fig. 2 summarizes the main topics
covered by different fraud detection review papers.

####  I-C1 Fraud Definitions and Categorization

Crawford et al. [13], and Dewang et al. [14] adopted an analytical approach to
identify fraud reviews. Dewang et al. [14] considered fraud reviews as one of
the many spam types including email spams, web spams (which refers to
techniques used to manipulate the rank and the orientation of web pages), and
blog spams (defined as messages posted randomly on different discussion
boards), while Crawford et al. [13] survey different types of fraud reviews,
using the definitions provided by Jindal et al. [15]:

  * •

Untruthful Reviews: Such reviews are written to promote/defame products
intentionally.

  * •

Fan Reviews: The second type is reviews written by extreme/radical fans of
specific brands or products.

  * •

Junk Reviews: These reviews contain no relevant information regarding the
targeted item; e.g., advertisements.

Crawford et al. [13] then focus on the detection of untruthful reviews,
similar to the current study. Mohawesh et al. [16] proposed a similar
categorization to that of Jindal et al. [15] and provided two examples to
explain the challenging task of labeling the reviews by human. To provide a
more general category for fraud detection, Paul et al. [17] categorized fraud
reviews as a type of opinion spam, alongside other categories such as
advertisements, unrelated random texts, and product questions.

Given all definitions and different types of fraud reviews investigated by
various review papers, some new types of fraud reviews are still missing. Bot
generated reviews, are still missing as an important type of fraud review
which are particularly difficult to spot [18]. In this paper we also consider
bot generated reviews as another type of fraud and investigate relevant recent
studies.

####  I-C2 Review Data

Heydari et al. [19] categorized review data into two major subcategories: 1)
the content of reviews and 2) the metadata of reviews. The content of the
reviews is used to extract text-based features, and the metadata is employed
to extract behavioral features (to be discussed in Sec. I-C4). Most datasets
provide both review text and metadata (e.g., rating, date of the review, user
ID, and item ID) alongside the ground-truth. The statistics of major datasets
is provided in Table II.

TABLE II:  Details of datasets in previous studies. Datasets | Reviews | Users | Items |  Metadata |  Ground-truth
---|---|---|---|---|---
Yelp | 608,598 | 260,277 | 5,044 |  review text, rating (1-5), date, user ID, item ID |  Yes (Yelp recommender system) [20]
Amazon | 53,777 | 42,655 | 6,822 |  review text, rating (1-5), date, user ID, item ID, profile name, feedback (number of likes) |  Yes (human judges) [15]
TripAdviser | 1600 | - | 20 |  review text, rating (like, dislike), date, user ID, item ID |  Yes (human judges) [10]
Tencent | 302,097 | 82,542 | 7,584 |  review text, rating (1-5), date, user ID, item ID |  Yes (near ground-truth) [21]
ResellerRating | 408,470 | 343,063 | 14,561 |  rating (1-5), user ID, item ID |  Yes (human judges) [22]
MT | 2,836 | - | - |  review text, rating (1-5), user ID, item ID |  Yes (Amazon Mechanical Turk) [23]
Dianping | 21,255 | 504 | 14,187 |  review text, rating (1-5), date, user ID, item ID |  Yes (human judges) [24]
Epinions | - | 189,028 | - |  rating (1-5), trust, user ID |  No [25]
WeiDai | 36,851 | - | - |  review text, user ID |  Yes (recommender) [26]
Lending | 18,405 | - | - |  user ID |  Yes (recommender) [27]
TREC | 707,664 | - | - |  review text, user ID |  Yes (TREC spam tracker) [28]
Expedia | 1,800 | - | - |  review text, review rating, user ID, item ID |  Yes (human judge) [29]
Google | 341,993 | 265,724 | 718 |  date, rating (1-5), user ID, item ID |  Yes (human fraud workers) [30]
Priceline | 4,427 | - | 118 |  date, review text, user ID, item ID |  Yes (Amazon MT) [31]
Weibo | 9,726 | - | - |  date, review text, user ID, item ID |  Yes (human judge) [32]
JD | 48,562 | - | 100 |  date, rating (like, dislike), user ID, item ID, uselessVoteCount, isMobile |  Yes (human judge) [33]
SWM | 1,132,373 | 966,842 | 15,094 |  date, rating (1-5), user ID, item ID |  No [34]

Mohawesh et al. [16], however, proposed a different categorization where
metadata is considered as a subcategory of the text-based features. The latest
review by Paul et al. [17] surveyed models based on the features extracted by
different approaches. Their features were extracted from both the review texts
and metadata.

Although previous survey papers provided a thorough review on the data that
could be utilized for fraud review detection, they do not consider new
modalities of data (IP, MAC address, etc.) that could be employed for fraud
detection. This multi-modal data has been utilized before for untruthful
reviews in Twitter [35]. We will discuss the potential of such data in
providing a better overview of the user activity on a platform in Sec. V. The
multimodal dataset helps the fraud detection algorithm to deal with more
complicated tasks such as the cold-start problem [36]. The “cold-start
problem” for fraud detection refers to the challenge of detecting fraud when
there is little or no previous data available to use for training a model.
This can make it difficult to accurately identify fraudulent activity.

Few recent studies [37, 38] also focused on components involved in fraud
review detection, failing in delivering a systematic framework used in fraud
review detection.

####  I-C3 Components of Interest

Heydari et al. [19], Dewang et al. [14], and Crawford et al. [13] also
categorized the fraudster detection tasks into different categories. Heydari
et al. [19] and Dewang et al. [14] considered two categories of single and
group fraudster detection, while Crawford et al. [13] mostly focused on two
different tasks of 1) review-based fraud detection and 2) user-based fraud
detection.

None of the current literature surveys examined the importance of targeted
items (products or services) in identifying the fraud contents and preventing
them to spread. In contrast to previous surveys, we consider targeted items as
another component of interest and examine their potential to address the
recent challenges on fraud detection, as utilized by Ji et al. [39] for the
cold-start problem.

####  I-C4 Features and Techniques

Heydari et al. [19] and Crawford et al. [13] elaborated on different features
for the detection task. Dewang et al. [14] specifically divided the features
into linguistic and non-linguistic features. Heydari et al. [19] then divided
fraud review detection techniques into two major groups: duplicating detection
methods and content-based methods. Crawford et al. [13], Dewang et al. [14],
and Vidanagama et al. [40] divided the classification approaches into
supervised, unsupervised, and semi-supervised. In contrast to previous
surveys, Vidanagama et al. [40] provided a critical review on features and
techniques through comparison and also considered network-based approaches,
overlooked by previous studies. Mohawesh et al. [16] surveyed the features
with more focus on the modality of extracted features. Mohawesh et al. [16]
were the first to survey word embedding techniques and explored different
studies to cover the fraud detection approaches that employed such techniques.
Paul et al. [17] specifically focused on the models based on the extracted
features. Paul et al. [17], therefore, categorized Fake Review Detection (FRD)
models into those which employed Content Similarity (FRD-CS), Writing
Footprints (FRD-WF), Behavioral Footprints (FRD-BF), Network Footprints (FRD-
NF), Rating Footprints (FRD-RF), and finally Collusion Footprints (FRD-CF).

Previous review papers considered different semi/unsupervised learning
approaches to show their importance in dealing with fraud review detection
when there is a lack of data. However, many recent studies utilized graph
representation learning to show their potential in addressing such a
challenge, such as Graph Convolutional Network [41], graph-based inductive
learning [42], etc. We will discuss such techniques in Sec. IV-B and Sec. IV-C
to provide a better overview of such techniques.

####  I-C5 Summary

Fraud detection is a relatively new and evolving area, some challenges are
only partially addressed while new challenges have recently emerged. For
example, there are also long-standing challenges that are still relevant (e.g.
lack of data), and new challenges to tackle (bot-generated reviews, cold-start
problem, etc.). Exploring all these topics under a unified framework helps
researchers to acquire new insights of future directions in fraud review
detection.

###  I-D Scope of This Review

Although previous studies have contributed to a better understanding of
fraudulent reviews and fraudsters, there is currently no comprehensive
framework that covers all aspects of fraud detection. The framework should be
simple and easy to understand while helping define the scope of different
studies and to clarify future challenges. Hereby, we devised a framework based
on three essential steps in fraud detection tasks, as illustrated in Fig. 1.

####  I-D1 Component Selection

In the first step, a component should be selected for information collection.
The framework includes three different components: review, user, and item.
Review, single-user, and group users are mentioned in previous review papers.
Previous review papers only considered the fraud and fraudster (single, group)
detection task, but not targeted item identification. The targeted items
recently attracted a lot of researchers’ attention [43, 20], and were detected
in combination with fraud review and fraudsters. Fig. 1 depicts the different
components which can be selected by a fraud review detection algorithm. This
review filled the gap by surveying works on targeted item detection. Another
overlooked emerging area of study includes bot review generation. Such
generators cause serious problems in social review platforms. Recent studies
on fraudsters indicated that they are now using bot-generated reviews. Bots
simply generate reviews similar to human written reviews, and simultaneously
bots hide their footprints [18].

####  I-D2 Feature Extraction

The second step extracts features from and for the selected component.

This Study • Providing a comprehensive framework
for fraud detection. • Extending the concept of fraud by introducing a new
type of fraud: bot-generated fraud reviews. • Creating a new perspective on
components involved in fraud review by introducing items as a separate
component. • Discussing recently emerged areas in fraud detection such as
cold-start, bot-generated reviews, and multimodal data. Mohawesh et al. [16] •
Analyzing the feature extraction task by word embedding • Summarizing the
existing techniques to traditional and deep learning techniques. • Conducting
a benchmark study to investigate the performance of different neural networks
Paul et al. [17] • Covering both basic research and commercial solutions. •
Exploring the models based on the extracted features. • Analyzing the reasons
behind the failure of the approaches in addressing fraud reviews. Vidanagama
[40] • Critical review of previous works from approach perspective • Proposing
an overview of improvements over previous works on fraud review detection
Dewang et al. [14] • Providing a comparative study on single/group fraudster
detection. • Giving a perspective of possible new techniques for future works.
Crawford et al. [13] • Performing a comprehensive study on components
(including review, and user) detection. • Devising a methodology for
conducting further investigations. Heydari et al. [19] • Categorizing studies
into three groups of detection methods on fraud review, single and group
fraudster. • Contrasting strengths and weaknesses of detection techniques.
20222021202020182015 Figure 2: The timeline of recent review papers.

Text-based features refer to features that are directly extracted from the
text using language models [44] or text statistics [45, 46, 31]. To improve
the review and user representations, review metadata is used to extract
behavioral features [15, 47, 48]. Text-based and behavioral features are
combined as “joint” features to achieve a better representation. Subsequent
studies also used the same joint representation to achieve better components’
representation [49, 20]. Given the fair performance of the lingo-statistic
features and their combination with other types of features they can still be
manipulated by fraudsters to escape detection. Recent advances in neural
language models, such as Word Embeddings (WEs) can be used to produce a vector
representation of review texts [50, 51, 52, 53] to overcome the limitations of
text-based lingo-statistic features in achieving a global representation. In
other words, lingo-statistic features suffer from a limitation to extract the
sentiments from different aspects of a text, while WE is capable of extracting
sentiment from various aspects of a given text. Fig. 1 provides an overview of
some of the utilized features in fraud detection. We extend on the previous
reviews by covering a wider range of review text representation learning.

####  I-D3 Classification/Scoring

In the third and final step, a classifier or scorer is applied to the
extracted features for the final labeling (scoring). Such approaches are
categorized as unsupervised, semi-supervised, or supervised. Due to the
scarcity of trusted labeled data in fraud detection, recent studies employed
unsupervised learning [42, 20] and semi-supervised [49] approaches. Supervised
learning is mostly applied on the Yelp dataset [50, 54] for fraud detection.
Fig. 1 displays an overview of fraud detection approaches. Deep learning is
the most recent popular supervised learning approach for improving fraud
detection accuracy [51, 55, 50].
To highlight the novelties of this survey, Fig. 1 presents each step of the
framework with a focus on the overlooked aspects in previous review papers. In
summary, this survey paper offers the following contributions:

  * •

For the first time, we introduce bot fraud generation as a newly emerging area
in fraudulent reviews.

  * •

We also extend the components to items targeted by fraudsters and explore the
studies in the target item detection.

  * •

We provide a more complete overview of open problems and identify three
categories of classical, ongoing, and hot topics to facilitate better
understanding of future directions of the fraud review detection research. In
each category, we then explain the usefulness of such approaches to address
future challenges, such as cold-start [36, 56, 57], bot-generated reviews
[42], transfer learning, fraud detection through multimodal data [35], end
etc.

  * •

We provide the first comprehensive survey that covers various research by
following a systematic framework for fraud detection in social review
platforms.

Figure 3: An overview of component selection step in a fraud detection
framework.

In the following sections, we first review the fraud detection studies
focusing on each of the three steps of our proposed fraud detection framework,
including components in Sec. II, features in Sec. III, and approaches in Sec.
IV, respectively. Next we provide discussions on the datasets, topic analysis
in fraud detection, and future directions based on the new identified
challenges, in Sec. V. Finally we conclude our survey in Sec. VI.

##  II First Step: Component Selection

Since the first review work by Jindal et al. [15] in 2008, the scope of most
studies are limited to fraud review and fraudster detection. Here, we broaden
the scope by introducing targeted item detection while expand the concept of
fraudsters to include software bots. Bots are capable of generating contents,
in a fashion that are highly similar to those written by human. Fig. 3. shows
the three key components involved in fraud review detection. In the following,
we discuss specific studies which considered a single component as their main
focus.

###  II-A Review

Early studies on fraud review detection mostly focused on fraud review
detection as the main task. For the first time, Jindal et al. [15] proposed a
categorization for different types of fraud reviews, namely, untruthful
opinions, reviews on brands only, and non-reviews (as discussed in Sec. I-C1).
Jindal et al. [15] employed different features such as review rating, review
feedbacks, rating deviation, review ranking (behavioral), and review feedback
(linguistics) to deal with fraud reviews. It is worth mentioning that Jindal
et al. split up features into three sets of categories; reviews, reviewer, and
product-centric features. The results on the Amazon dataset showed an AUC of
98.7% for fraud review detection.

Jindal et al. [15] were the first to provide a list of employed features for
fraud review detection. Afterward, researchers proposed various methods to
improve the performance of the fraud review detection.

Singleton fraud reviews are reviews written by the same person using different
account names. Sandulescu et al. [58] was the first to propose a framework to
spot singleton reviews. The proposed framework specifically employed two
different methods: first, the review similarity; second, the topic similarity
using bag-of-words and bag-of-opinion-phrases. For topic modelling, the
proposed framework utilized Latent Dirichlet Allocation (LDA). The proposed
approach showed a maximum precision of 80% on the Yelp dataset.

Barbado et al. [59] proposed a framework that extracts several types of
features for fraud review detection. The proposed features include personal
features (e.g., a profile description, bookmarks, and updates), social
features (e.g., popularity, and compliments), review activity (e.g., review
count, negative ratio, and self-deviation), and trust (e.g., relative
deviation, and content similarity). An explanation of a selected few of
features is provided in Table III.

TABLE III: An overview of some of the features employed by Barbado et al. [59]. H/L depicts if a High/Low value of the the feature is more likely to be associated with the fraud. Features |  Explanation |  H/L |  Formula
---|---|---|---
Review Count |  Number of reviews by a specific user |  H |  $N_{R_{u}}$
Negative Ratio |  Ratio of negative review vs. total number of reviews |  H |  $\frac{N_{Rating_{u}=1,2}}{N_{R_{u}}}$
Self Deviation |  Deviation of ratings given by the fraudster from the rating average value |  H |  $\frac{1}{N_{R_{u}}}\sum_{i}(Rating^{i}_{u}-avg(Rating_{u}))$

Barbado et al. utilized an adaBoost classifier which yielded an F-measure of
82% on the Yelp dataset. With the advance in context-aware applications, Ruan
et al. [60] proposed a Geolocation-based Account Detection Model (GADM) to
improve the fraud review detection performance. GADM utilized geolocation
information alongside account information (e.g., review rating) to capture
location-based information in three phases. In the first phase, account
features and geolocation features were extracted from the reviews and used as
input. The geolocation information was obtained from visited restaurants and
hotels by users. The geolocation information is then fed to a Long-Short Term
Memory (LSTM) to model the user location series. A classification step is then
carried out on account information and the outputs were then combined and fed
to SVM for final classification. GADM showed an accuracy of 85.8% on the Yelp
dataset.

He et al. [26] proposed a scheme considering the temporal relationship between
reviews. He et al. [26] proposed a PU learning based on positive (P) samples
(fraud reviews) and unlabelled (U) ones. The PU learning refers to an
approach, where unlabeled samples are scored based on the samples with
positive labels, in a binary classification task. The proposed PU scheme
includes three consecutive steps designed to score reviews being fraud or
genuine.

In the first step, the time interval between every two reviews was used as a
feature to label unlabelled reviews as Real Negative (RN) reviews. Next, an
SVM classifier is trained using the Positive (P) and RN set. Finally, text-
based features were combined with outputs of previous outcomes into an
Expectation-Maximization (EM) algorithm for the final clustering. Data
collected from the WeiDai-Financial444http://weidai.investorroom.com/ website
were used as the main dataset. The proposed approach showed an F1-measure of
75.3%.

The analysis of studies on fraud review detection shows the importance of
spatial and temporal modeling of reviews in a social review platform to detect
fraud reviews.

###  II-B User

Initially, fraudsters worked in isolation when using a social review platform.
As businesses recognised the significant influences of social reviews on sales
and profits, fraudsters started working in groups to gain more impact. With
the advances in Deep Learning techniques in recent years, bot fraudsters
started to play a non-negligible role in generating and propagating fraud
reviews. In this section, we take into account these new trends and focus on
fraudster group and bot fraudster detection after a review of the traditional
single fraudster detection.

####  II-B1 Single User

Single user refers to a fraudster who actively is involved in fraudulent
activities as an individual. Single fraudster detection techniques mostly rely
on a users’ behavior pattern and their actions. Jiang et al. [61, 62] proposed
a suspicious behavior formulation through a combination of matrix
decomposition (for finding unexpected values) and KL Divergence for K-mode
speciousness. A matrix is initialized first with each user as the row and
features as the columns. For each user, the ID, IP, time of the written
reviews and retweet comment were used to find fraud reviews on Twitter.

Chiu et al. [63] also utilized user behavior analysis to find suspicious
activity, using multimodal data. Multimodal data refers to data gathered from
different sources such as security devices, networks, servers, and
applications. Chiu et al. [63] employed three modalities [64] from data which
refer to data derived from different layers of the review writing process
(from IP address at the lowest layer to review text at the highest layer).
Twitter messages obtained from streaming API, certain user data (e.g., User
ID, Tweet ID, and Time), Network Information (e.g., IP Address), and Device
Information (e.g., GPS position). Chiu et al. [63] characterized fraudsters as
users with the same IP tweets who retweet with a different username for
different purposes (political or commercial manipulation). The multimodal data
was then mapped to a 3D tensor, representing data evolution through time. The
matrix in each time step represents the user ID, review ID, and the date of
the written review as data modalities. Chiu et al. [63] provided an intuition
of how modeling the behaviors of the user through time helps to improve
fraudster detection.

To detect single fraudsters, Kumar et al. [65] proposed to first identify
possible fraudster groups to find single fraudsters. This is based on the
observation that behavioral clues on singleton reviews are scarce. To obtain
information on a group of reviewers’ collective behavior, a review-item matrix
is constructed. The output of the proposed approach is a score given to each
fraudster group. They achieved a recall of 88.79% on the YelpZip dataset.

The graph Convolutional Network (GCN) was adopted by Wang et al. [21] to
detect fraudulent users in review platforms in a framework called FdGars. Wang
et al. [21] categorized fraudsters based on their motivations into three
types: camouflaged users, crowdsourcing (group fraudsters), and fraudsters.
FdGars performs both tasks of fraud and fraudster detection using review text
statistics (e.g., review length and review symbol number) and user-based
features (e.g., Review Quantity (RQ), Time-based Quantity Distribution (TQD),
and Score-based Quantity Distribution (SQD)).

TABLE IV: An overview of some of the features employed by Wang et al. [21]. H/L depicts if a High/Low value of the feature is more likely to be associated with fraud. Features |  Explanation |  H/L |  Formula
---|---|---|---
Review Length |  Length (L) of review as the number of words |  L |  $L(R^{i}_{u})$
Review Symbol Number |  Number of non-alphabetic characters (NAC) in a review |  H |  $N_{NAC_{u}}$
Review Quantity |  Number of reviews written by a fraudster |  H |  $N_{R_{u}}$
Time-based Quantity Distribution |  Distribution of the number of reviews over time |  - |  N/A
Score-based Quantity Distribution |  Distribution of the number of reviews over different ratings |  - |  N/A

Table IV lists these features. The behavioral features connected users as
nodes in a graph based on the users’ proximity. The users’ proximity was
calculated based on the users’ similarity in terms of extracted features. The
GCN then labeled the reviews and users based on the similarity between them
through a semi-supervised approach. FdGars achieved a performance of 93.8% for
F1-measure on a dataset collected from Tencent
Inc.555https://www.tencent.com/en-us with 302,097 reviews written by 82,542
users on 7,548 different applications. Recently, Danilchenko et al. [66]
employed few shot learning in combination with a classical and a graph-based
approach (specifically, belief propagation) and demonstrated that the
combination of the two outperformed the individual approaches. Danilchenko et
al. [66] first created a network of users connected to each other based on a
set of products they have co-reviewed. To obtain the ground truth labels for
reviewers, the proposed approach employed active learning via few shot
learning. The potential of each user to be benign or fraudsters was set to
[0.5,0.5]. Each user was represented with a feature vector based on features
such as Positive Ratio (PR), Rate Deviation (RD), etc. The experiments showed
that the proposed approach outperformed the previous study with an AUC of 72%
on the Yelp dataset.

In summary, the GCN provided a great opportunity for researchers to
characterize the behavior of users in social review platform. Recently,
multimodal data was also used to aggregate different aspects of user activity
and hence improved the performance of fraudster detection.

####  II-B2 User Groups

A fraudster group refers to a group of users coordinating to attack the same
item. Most studies focused on employing graphs to model a group of users.
Candidates of a potential fraudster group are first generated followed by a
refinement step to improve the group’s constitution.
Graphs in the fraud detection task are typically categorized into three sub-
categories based on the node type:

  * •

Monopartite: The nodes are from the same type (users, or reviews) [67, 49, 68]

  * •

Bipartite: The nodes are from two different types (both users and reviews, or
users and items) [34]

  * •

Multipartite: The nodes are from more than two different types (users,
reviews, and items) [20].

The connections between the nodes are defined based on the downstream task.

NEST and BEST were proposed by Zhu et al. [68] to address the overlooked small
dense groups (modeled as a complete graph in the fraudster group detection
task). A bipartite graph was utilized to model the users as a node type and
users’ activities as another node type. Users’ activities are represented
based on their social profile. Users with similar activities are more probable
to be in a small group of fraudsters, representing explicit relations.
Implicit relation was then defined as a relation between two users obtained
through other users with similar activities. NEST and BEST both employ an
iterative scorer to score the groups. BEST achieved an F-measure of 71% on the
Yelp dataset.

Bitarafan et al. [69] employed a Heterogeneous Information Network (HIN) to
utilize the merits of a graph in both the feature and classification step. The
proposed approach; $SPGD\\_HIN$; employed a bi-connected candidate group
detection to detect the users with at least one co-reviewed item.

TABLE V: An overview of some of the features employed by [69]. H/L depicts if a High/Low value of the feature is more likely to be associated with fraud. Features |  Explanation |  H/L |  Formula
---|---|---|---
Group Time Window |  The time (T) window in which the members of a group write the reviews |  L |  $Max([Max(T_{R_{u}})-Min(T_{R_{u}})]\forall u\in group)$
Group Product Tightness |  Number of common products with reviewers in a group |  H |  $N_{R_{u-p}\bigcap R_{u^{\prime}-p}}$

The weight between two users was therefore computed based on the time-interval
between the reviews written by the users. The users with strong ties (similar
rating, and reviews in a time burst) were defined as a bi-connected fraudster
groups. Several features were extracted such as Group Time Window (GTW) and
Group Product Tightness (GPT). Table V describes these features. Next, the
metapath concept was employed to obtain the final weight between each of the
two groups. Groups were labeled using a semi-supervised approach based on the
calculated weights. The results yield a precision of 70% on the Yelp dataset.
Similar to single fraudster detection multimodal data were incorporated
alongside graph-based methods to improve the detection task.
GraphRfi, proposed by Zhang et al. [41], utilized different features such as
the number of rated products and the length of a username. Extracted features
were combined with the rating of each review into a single vector and then fed
to a Multi-Layer Perceptron (MLP) for the final prediction. GraphRfi showed an
F-measure of 99% on the Yelp dataset.

In summary, the earlier studies on fraudster group detection mostly relied on
graph-based techniques to model the relations between members in a group,
while later researches employed graphs to learn the vector representation of
users. Most recent studies focused on multimodal data to better represent a
group and for an improved performance.

####  II-B3 Bot Fraudsters

As deep learning has advanced, fraudsters have recently started using bots to
generate fraudulent reviews. In contrast to human-written fraud reviews, bot-
generated reviews can be produced in a high volume, cutting the cost of human
fraudster employment. Most importantly, bot-generated reviews leave no
behavioral trace, as such bots can manipulate the behavioral clues (e.g.,
number of written reviews in a time burst, date of written reviews, the volume
of reviews, review rating, etc.), which are often used in behavioral feature
extraction. In this section, we explore the studies on the newly introduced
topic of bot-generated review detection. Bot-generated attacks were first
investigated by Yuanshun et al. [18] using a pattern analysis method on the
generated texts. Yuanshun et al. [18] proposed an approach to generate reviews
based on the character distribution modeling with a simple Recurrent Neural
Network (RNN). The generation process followed three consecutive steps. In the
first step, the RNN was trained using characters instead of words, due to the
low computational cost and the memory required for the training. Next, reviews
were generated by the model. The limited size of the model led to information
loss during back propagation. Hence, in the third step, the generated reviews
were customized by replacing certain words with domain terminology.

To detect generated reviews, an RNN classifier was trained based on the
character distribution of the review test. To this end, two separate RNNs were
trained, one with the generated reviews, and the another with the real
reviews. The reviews in the test set were then fed to both models and the
probability of each review being a fraud or real was calculated through a log-
likelihood measurement. The likelihood was using a similar approach to that of
the Siamese network where the outputs of two different trained neural networks
are given to a scorer to calculate the final score. Fig. 4 shows the framework
proposed by [18]. The results of the proposed approach showed an F-measure of
86% on the Yelp dataset.

Figure 4: The overall framework proposed by [18].

Afterwards, several studies utilized Generative Adversarial Networks (GAN) to
simulate the process of the review generation through bots. GAN was proposed
by Goodfellow et al. [70] to originally generate captions for images. The GAN
consists of two main blocks: a Generator and a Discriminator. In bot review
generation/detection context, before training the blocks, the generator and
the discriminator are pre-trained using the samples from the training set
derived from the TripAdvisor dataset. In the adversarial training phase, the
pre-trained generator produces negative samples for the discriminator. In a
zero-sum game between the two blocks, the generator generates fake samples and
the discriminator learns to discriminate between real samples and fake ones.
Both models are updated based on the backpropagation loss from the
discriminator. Such a structure makes the GAN a promising choice for both bot
generation and fraud detection tasks.

Aghakhani et al. [50] proposed FakeGAN which employed a generator and two
Convolutional Neural Networks (CNNs) as discriminators, one to discriminate
between the generated reviews and human written fraud reviews, and the other
one to discriminate between the set of fraud reviews (the combination of
generated reviews and human written fraud reviews from the dataset) and real
reviews. Two discriminators were proposed to address the limitation of the
GANs in handling the “mode collapse” problem. The mode collapse problem refers
to a situation where the generator generates the reviews only from a single
mode of distribution due to the multi-dimensionality of the data.

Before the adversarial training, the generator was pre-trained using Maximum
Likelihood Estimation (MLE) and the discriminators were pre-trained using a
cross-entropy loss function. FakeGAN was evaluated on TripAdvisor with 1600
reviews, containing 800 fraud reviews and 800 real reviews, and showed an
accuracy of 78% (See Sec. V-B).

To better simulate bot reviews, Shehnepoor et al. [67] proposed ScoreGAN, a
GAN-based approach where the generator utilized review ratings to improve the
quality of the generated reviews. Their objective function introduced an
additional term to maximize the correlation between a generated review for a
given rating. Such a generator improves the quality of the generated reviews,
which ultimately enhances the detection accuracy. ScoreGAN showed an accuracy
of 84% and 77% on the Yelp and TripAdvisor datasets, respectively.

In summary, although only a few studies investigated bot review identification
as fraud detection, recent approaches have demonstrated promising results. The
use of GAN significantly improved the detection performance through bot review
generation and the simultaneous training of one or two discriminator(s) to
detect bot-generated reviews.

###  II-C Item

Some services or products may be more prone to fraud review attacks than
others, referred as targeted items. As soon as a new product is introduced
into markets, fraudsters may start to propagate fraud reviews. Detecting
targeted items, therefore, is as important as fraudster and fraud review
detection. Target item detection can indirectly help improve fraudster and
fraud review detection. However, few studies investigated target item
detection.

Given the significance of item classification in fraud detection, FraudEagle,
proposed by Akoglu et al. [34], uses Loopy Belief Propagation (LBP) to
propagate the fraudulent score through users and items. LBP is an iterative
message passing, shown to be effective for various real-world applications
[71]. As existing datasets contained no groundtruth, Akoglu et al. [34]
removed the users with a high probability of being fraudsters and then
measured the items’ rating difference before and after removing the suspected
groups. Akoglu et al. [34] concluded that fraudsters significantly affected
the average rating given to target items. Thus, the target items were
identified by removing fraudsters while observing the impact on the SoftWare
Marketplace dataset (SWM)666http://odds.cs.stonybrook.edu/swmreview-dataset/.

Another work that uses item features is SPeagle [34]. To improve the fraudster
detection performance, SPeagle [20] used both metadata and text to extract
features from reviews, users, and items. The extracted vector (the combination
of behavioral features such as negative ratio, positive ratio, and linguistic
features such as content similarity) from review, user, and item was used to
calculate prior knowledge for each component. The prior knowledge was then
utilized to initialize a representation of each component in a multi-partite
graph. Nodes in the graph represent user and items. Each edge represents a
review written by a user on an item. LBP was employed to predict fraud
reviews, fraudsters, and target items. SPeagle showed an AUC of 69.07% and an
AP of 42.45%, on the Yelp dataset.

Ji et al. [39] utilized target item detection to improve fraudster group
detection performance. Ji et al. [39] employed the item-related features
(product rating distribution, product average rating distribution, and
suspicious score) to detect candidate fraudster groups in the Amazon
dataset777https://jmcauley.ucsd.edu/data/amazon/. To this end, the fraudster
group features (group rating deviation, group size, group review tightness,
group one-day reviews, group extreme rating ratio, group co-Activeness, and
group co-active review ratio), individual fraudster features (ratio of extreme
rating, rating deviation, the most reviews one-day, review time interval,
account duration, and active time interval reviews), and item related features
were first extracted. Then, a threshold was applied to the overall score,
resulting in a representation to initially determine the targeted items. A
Kernel Density Estimation (KDE) method was then applied to calculate the
burstiness of items. KDE is a technique that could asymptotically converge to
any density function with sufficient samples. As such, KDE was employed to
model the review sequence of a target item to find groups of reviewers in a
review burst. A review burst refers to a sudden increase in the popularity of
a product due to a potential fraud attack. Next, candidate groups were
determined from the KDE output. The items with burstiness value over a
threshold were considered target items. The approach demonstrated an
F1-measure of approximately 75% on the Amazon dataset.

###  II-D Summary

To better illustrate the evolution of research on component detection, we have
provided a timeline of previous studies on various components in Fig. 5. As
new techniques have advanced, more researchers have focused on components as
the primary subject of study. Hence, Fig. 5 shows a boost toward studies
focusing on each of the three components (especially between 2019-2021).

Week 020172018201920202021Jindal et al.Wang et al.Akoglu et al.Shebuit et
al.Yuanshun et al.Chiu et al.Aghakhani et al.kauffmann et al.Barbado et
al.Wang et al.Zhu et al.Zhao et al.Bitarafan et al.Shehnepoor et al.Zhang et
al.Ji et al.He et al.Ruan et al.Wen et al. Figure 5: The volume timeline of
the studies on different components in fraud review. Diameter of each circle
is proportional to the number of studies for one year period [2017-2018,
2018-2019, etc.]

##  III Second Step: Feature Extraction

After choosing a component of study, features of the selected component need
to be extracted, either from the review text or metadata. Features extracted
from the review text are referred to as text-based features, while features
making use of metadata are referred to as behavioral features. To achieve a
better representation, text-based features and behavioral features can be
concatenated or jointly learned through deep learning to obtain joint
features. Fig. 6 displays the detailed steps involved in the feature
extraction stage.

###  III-A Text-based

Features extracted from the text for fraud detection can be grouped into two
categories, namely lingo-statistics features and vector space features (i.e.,
word embeddings).

####  III-A1 Lingo-Statistics

Lingo-statistics text-based features were proposed by Lai et al. [28] to
detect different types of Spam contents, including fraud reviews. As one of
the first text-based features, a unigram language model was developed based on
Part of Speech (POS) tagging. To calculate the similarity between two
different reviews, the Kullback-Leibler (KL) divergence measure was used,
where the likelihood of a review generating the contents of the other review
was used. The performance of the proposed approach was evaluated on the TREC
dataset and showed a precision of 94%.

Mukherjee et al. [72] also employed Part Of Speech (POS) tagging to extract
the bigram and unigram features from review text based on a specified
frequency. An SVM model was trained on the extracted POS features and showed
an accuracy of 85.1% on the Yelp dataset.

Banerjee et al. [29] extended the concept of the lingo-statistic text-based
features using understandability, level of details, writing style, and
cognition indicators. For each, a set of features was proposed to describe the
review. Several classifiers were employed such as Logistic Regression (LR),
Decision Tree (DT), Neural Network (NN), Naive Bayes (NB), Random Forest (RF),
Support Vector Machine (SVM), and voting. A dataset of 900 genuine reviews was
collected from different hotel websites including Hotels.com, Expedia.com, and
Agoda.com. To provide positive samples, 60 fake reviews were also deliberately
written by experienced reviewers. LR yielded the best performance with an AUC
of 81.5% on the Expedia dataset. Though singletone text-based features (i.e.,
features from a single review) provide a good level of characterization, the
similarity between reviews (known as pair-wise features) provided a more
powerful means for fraud detection and is considered as one of the most
important lingo-statistic text-based features.

Figure 6: An overview of the feature extraction stage in a fraud detection
framework.

With many lingo-statistic text-based features proposed in the early years,
Crawford et al. [23] decided to evaluate the importance of each word in a
review for the fraud detection task. To this end, Crawford et al. [23]
designed an approach to determine the keywords for the fraud detection task.
Several feature selection techniques were utilized to determine the importance
of each word in a review text for the fraud detection task. The techniques
included word frequency, Chi-Squared (CS), and Mutual Information (MI). Then
the presence of the 100 most frequent words in a review was given as a feature
vector. The word vector was then fed to different classifiers such as LR, NB,
SVM, etc. The best result was achieved for SVM on the Mechanical Turk (MT)
dataset with an AUC of 87%.

As the most recent study employing lingo-statistic features, Abri et al. [73]
extracted new linguistics features such as lexical diversity, emotiveness,
etc., to detect fraud reviews. To evaluate the performance of different
classifiers, the extracted features were fed to an SVM, Naive Bayesian, Random
Forrest, etc. The best performance was demonstrated by the Multi-Layer
Perceptron (MLP) with an accuracy of 79.09% on reviews crawled from online
restaurants.

The obvious shortcoming of lingo-statistic text-based features is that
indicators researchers adopted can easily be manipulated by fraudsters.
However, some lingo-statistic text-based features such as language models, are
employed to reduce the need for explicit indicators to deal with fraud
detection. A language model can be employed in different ways as lingo-
statistic features. Lai et al. [28] employed the unigram model to calculate
the likelihood of each review is a fraud, while Shebiut et al. [20] employed
the language model to represent each review as a bag-of-bigrams and then
calculate the similarity between two reviews. With the advent of Deep
Learning, different neural networks such as RNN were used to consider the
language model in a review and improve the detection task.

####  III-A2 Word Embeddings

Lingo-statistic text-based features suffer not only from sparsity and
subjectivity but also from easy manipulation by fraudsters. On the other hand,
the newly introduced Word Embedding (WE) techniques utilized vector
representation of words to overcome the such limitation. Yafeng et al. [51]
claimed that a genuine review contained more contextual embedding, i.e., it is
more informative than fraud reviews, as demonstrated in previous studies [74].

Accordingly, Li et al. [24] extracted different text-based features such as
review content similarity (pair-wise features) between two different reviews
and then extracted a WE for each one. Emotion modeling was proposed based on
the sentiment of the review text. The sentiment diversity, i.e., means square
deviation of the negative, positive, and neutral word ratios is obtained based
on their squared difference. The combination of all text-based features was
then fed to different classical classifiers for labeling. The performance
showed an F1-score of 93% on the Yelp dataset with a Decision Tree (DT) as the
classifier, shown to be more effective than lingo-statistic features. Given
the success of WE in extracting sentiment, researchers began to employ WE in
the fraud detection task. Zhang et al. [75] claimed that the reviews written
by fraudsters were different from real ones since fraudsters typically could
not reflect their experience of using the product in their reviews,
potentially due to the lack of experience with the products.

Ren et al. [51] demonstrated that the Recurrent Convolutional Neural Network
(RCNN) outperformed traditional classifiers. The RCNN takes distributed word
embeddings as an input to learn a continuous document representation for each
review. The Continuous Bag of Words (CBOW) word embeddings were obtained using
Word2Vec pre-trained on the dataset and then fed to an RCNN for extracting a
document representation. Finally, the representation is fed to a softmax layer
for classification. The results showed an accuracy of 83.6% on the Mechanical
Turk dataset. Jia et al. [76] also employed term frequency and LDA (Latent
Dirichlet Allocation) alongside WE. LDA is an approach that extracts review
topics by highlighting important words (the most frequent ones). To calculate
the term frequency, 5000 of the most frequent words (using unigram) were
extracted from reviews and each word’s frequency was calculated. The Word
Embeddings (WE) were pre-trained for each word using the skip-gram model [77]
and then an average of the words’ embeddings was used as a vector
representation of a review document. Jia et al. exploited 5 topics for fraud
and genuine reviews and each topic was described in 8 words. Such a topic
modelling helped to find the most popular trends among fraudsters and genuine
users. A Multi-Layer Perceptron (MLP) was applied to the final representation
to achieve an accuracy of 81.3% on the Yelp dataset.

In summary, the most recent studies employed word2vec techniques for fraud
detection to extract WE for reviews. The proposed approaches extracted WE
either independently from the classification phase in a pre-training phase
(e.g., skip-gram, Glove, etc.) to obtain the representation, or employed an
end-to-end deep learning technique (e.g., RCNN). To improve the performance of
WE in improving fraud detection accuracy, the representations were also
combined with behavioral features. As the most recent approach, new pre-
trained architectures are used to provide finetuned representations, which can
be easily transformed to a different scope. Bidirectional Encoder
Representations from Transformers (BERT) [78] has been used to bi-
directionally learn the representations using transformers. The key difference
is that BERT considers both directions for learning the embeddings instead of
either left or right. Such techniques can be helpful for obtaining a better
representation in the fraud detection task.

###  III-B Behavioral

Behavioral footprints play an undeniable role in detecting fraud review. Such
features can be used to represent a user, a review, or an item.

With the demonstrated importance of rating as a behavioral feature in early
studies, Luca et al. [79] employed the rating from reviews and applied a
regression model to determine the fraudulent score of the reviews. As the
review rating was the only feature employed by Luca et al. [79], the
regression ($R^{2}$) value between review ratings and predictions was
calculated to evaluate the performance of the proposed approach. The results
demonstrated a regression of 0.68 between the review rating and the
probability of the review is a fraud on the Yelp dataset.

Goswami et al. [80] proposed an approach with user-based features as the input
to an artificial neural network (ANN) to spot fraud reviews on a dataset
crawled from the Yelp website. Goswami et al. employed different behavioral
features such as photo count, compliment, friend count, tips, followers, and
funny and useful votes as extracted features. To determine the importance of
each new feature a decision tree was used. The framework showed a precision of
91.53% and a recall of 99.98% on a manually collected dataset.

To incorporate effective behavioral features, an Unauthorized Optimization
based De-Anonymization (UODA) approach was proposed by Hernandez et al. [30]
to deal with fraudsters with multiple accounts on social review platforms.
Inter-review time was also employed as a key feature in determining fraudsters
(also referred to as burstiness in different studies). Rating difference
between two reviews was used as another behavioral feature to reflect the
correlation between two different reviews. An iterative classifier was then
applied to the features extracted for each user to calculate the similarity
between different users and then calculated the fraudulent score for each
user. The UODA showed an F1-score of 93.84% on the Google play dataset.

Shaalan et al. [81] proposed a two-step approach concentrating on the key
features of fraudsters such as adding redundant information in written review
text or writing reviews in bursts. In the first step, a Deep Boltzmann Machine
(DBM) was used as the aspect-level sentiment model. In the second step, a Long
Short Term Memory (LSTM) was applied to the extracted sentiment aspect-level
representation. The output of the LSTM was the label of a review to be
fraudulent/genuine. The approach shows an accuracy of 80.85% on the Yelp
dataset.

In summary, despite being utilized in fraud detection since the early studies,
behavioral features are still considered as one of the most effective
indicators for the fraud detection task. However, behavioral features may have
limitations in handling bot-generated reviews. Different modalities of data,
such as IP, MAC, etc., may provide a better overview of users’ activity in
social review platforms.

###  III-C Joint

Joint features refer to the combination of text-based and behavioral features;
through either a simple concatenation or a joint learning process. Joint
features were proposed to improve the representation of the components in
social review platforms.

TABLE VI: An overview of some of the features employed by Kumar et al. [82]. H/L depicts if a High/Low value of the feature is more likely to be associated with the fraud. Features |  Explanation |  H/L |  Formula
---|---|---|---
Review Count |  Number of reviews written by a user |  H |  $N_{R_{u}}$
Review Gap |  The time gap between written reviews of a user |  L |  $T_{R^{i}_{u}}-T_{R^{i-1}_{u}}$
Entropy |  The difference between users’ reviews |  L |  N/A

Given the promising performance of the joint representation, researchers
recently employed the joint learning of the text-based and the behavioral
representations to address challenging problems such as the cold start
problem.

The cold-start problem was first investigated by Wang et al. [36] and refers
to the limitation of the filtering algorithm to detect fraud reviews written
by a new user. Wang et al. [36] was presumably the first study to address the
cold-start problem. This is a challenging task since there is no information
history of a new user, resulting in the failure of the detection algorithm to
detect new fraudsters.

Wang et al. [36] claimed that features proposed by early studies fail to
describe user behaviors. Therefore, a TransE model was employed to encode a
graph structure between an item, a user, and a review (as a
head/translation/tail relation). Lingo-statistic text-based features (e.g.,
review length and maximum cosine similarity) and behavioral features (e.g.,
rating deviation) were extracted for components in the Yelp and Amazon
platform. Continuous Bag of Words (CBoW) was employed as a Word Embedding
technique to obtain the word embedding from the review text. The extracted
features were then jointly learned through an objective function on both the
text-based and behavioral features. Finally, a CNN was used to classify the
users as genuine/fraudster on the Yelp dataset. The results demonstrated an
accuracy of 58.3% on the Yelp dataset.

To address the limitations of the framework proposed by Wang et al. [36], an
attribute-based framework, namely AEDA (Attribute Enhanced Domain Adaptive),
was proposed. Three types of relations were defined: attribute-attribute,
entity-attribute, and entity-entity. Pairwise features were extracted to
better handle the cold-start problem and the objective function was defined
based on the relations in the TransE model. AEDA demonstrated an accuracy of
80.0% on the Yelp dataset.

The joint representation learning of the text-based and behavioral features
has also been broadly employed to address the data challenges in recent years.
Kumar et al. [82] proposed an unsupervised approach to overcome the
limitations of supervised learning. The proposed approach first fits the best
distribution to the different behavioral features such as Review Count (RC),
Review GAP (RG), and rating entropy as shown in Table VI. The length of the
review (i.e., the number of words) was also extracted as a lingo-statistic
text-based feature. To incorporate the inter-dependencies between reviews,
extracted features were modeled as different Dirichlet distributions. The
distributions were modeled using a Gaussian Mixture Model. According to Kumar
et al., a fraudster was more likely to be deviant in a distribution compared
to a genuine user. The proposed approach demonstrated an AUC of 70% on the
Yelp dataset.

As one of the most recent studies, Xiang et al. [83] proposed a framework to
represent linguistic information using BERT. Behavioral features such as the
Maximum Number of Reviews (MNR), Positive Ratio (PR), Negative Ratio (NR),
etc. were extracted from the reviews and categorized into two categories of
product-based and user-based features. Such features were then fed to the
Graph Convolutional Network (GCN). Both BERT and behavioral features were then
fused into one global feature and fed to a Softmax layer for final
classification. The proposed approach demonstrated an accuracy of 69.9% on the
Yelp dataset.

The joint representation extraction provided an opportunity to deal with more
challenging problems recently introduced in social review platforms. However,
the performance of a framework with a joint representation of components as
the input highly depends on how the features are combined.

TABLE VII: Studies based on feature categorization. Feature Categories |  Features |  Paper |  Component
---|---|---|---
Text-based |  Lingo-stats |  Unigram language model |  Lai et al. [28] |  Users
POS-based features |  Mukherjee et al. [72] |  Reviews
Pairwise features |  Xu et al. [84] |  Users
puasality, lexical diversity, emotiveness |  Abri et al. [73] |  Reviews
WE |  WE through skip-gram, positive and negative ratio |  Li et al. [24] |  Reviews
CBOW |  Ren et al. [51] |  Reviews
Skipgram |  Zhang et al. [75] |  Users
Skipgram + word frequency |  Jia et al. [76] |  Reviews
Behavioral |  The Ratio of First Reviews, Rating Extremity, etc. |  Mukherjee et al. [85] |  Reviews
review rating |  Luca et al. [79] |  Reviews
photo count, compliment, friend count, tips, followers, etc. |  Goswami et al. [80] |  Reviews
inter-review time, rating difference, etc. |  Hernandez et al. [30] |  Users
review rating |  Li et al. [86] |  Users
Burstiness |  Shaalan et al. [81] |  Reviews
Joint |  unigrams, bigrams, second-person pronouns ratio, number of objective and subjective words in a text, and the cosine similarity between two review texts, rating deviation |  Li et al. [46] |  Reviews
inter-review time, rating difference, etc. |  Hernandez et al. [30] |  Users
Review Length (RL), Maximum Cosine Similarity (MCS) |  Wang et al. [36] |  Reviews
rating difference, date difference, CBOW |  You et al. [56] |  Users
Review Count (RC), Review GAP (RG), and Rating entropy, Review Length (RL) |  Kumar et al. [82] |  Users
|  Maximum Number of Reviews, Positive Ratio, Negative Ratio, BERT |  Xiang et al. [83] |  Reviews

###  III-D Summary

To provide a general overview of the evolution of features, a volume timeline
is depicted in Fig. 7 showing the most recent studies on different types of
features for fraud detection. Earlier studies (before 2017) focused on
proposing new features, while deep learning provided the opportunity to use
neural models to represent features in vector spaces. Fig. 7 shows a boost in
the number of studies that focused on features in 2018, with more studies
concentrating on features in the subsequent years. A summary of employed
features is shown in Table VII

Week 02017201820192020Lai et al.Banerjee et al.Xu et al.Crawford et
al.Mukherjee et al.Li et al.Li et al.Luca et al.Ren et al.Wang et al.You et
al.Jia et al.Hernandez et al.Zhang et al.Li et al.Kumar et al. Figure 7: The
volume timeline of the studies on features employed in fraud review detection.
Diameter of each circle is proportional to the number of studies for one year
period [2017-2018, 2018-2019, etc.]

##  IV Third Step: Classification/Scoring

In the final step of the proposed fraud detection framework, the extracted
features or feature representations are used to classify each component. The
approaches are categorized into three main sub-categories; supervised, semi-
supervised and unsupervised. These categories can output the binary labels
indicating the class of the component, or ranked scores showing the
probability of the component belonging to a specific class. An overview of
approaches is given in Fig. 8.

Figure 8: An overview of different modules, feature types, and outputs used in
a fraud detection framework.

###  IV-A Supervised

The supervised approaches use labeled samples to train the model. Early
studies mostly employed supervised learning to classify the components.
Nonetheless, early studies [15, 87] used datasets labeled as near-ground-truth
or preferred to use a small set of manually labeled samples. More recent
approaches focused on unsupervised and semi-supervised approaches to overcome
several limitations such as the lack of labeled data, uncertainty of near-
ground-truth datasets, etc. Supervised approaches employed either classical
techniques or deep learning to perform the classification.

####  IV-A1 Classical

Early studies adopted classical classifiers such as the Naive Bayesian Model,
SVM (Support Vector Machine), Multi-Layer Perceptron (MLP), and Logistic
Regression (LR). Such classifiers utilized samples with ground-truth labels
from human experts to train the detection models.

Peng et al. [88] proposed a relationship-based method to detect fraudsters.
Hence, Peng et al. [88] considered the reviews as a relationship between the
item and the user in a network. The sentiment of each review was obtained as
the first step in the feature extraction step. The sentiment score of the
review was then compared to the rating of the review as the first indicator,
called Interior Difference (ID).

The second and third indicators were Rating Deviation (RD) and Sentiment
Deviation (SD), respectively. The final fraudulent score of a user was
calculated by a linear regression classifier. The experiments on the
Reseller_rating dataset showed an NDCG of 90%.

Previous studies [31, 89, 88] revealed the potential of classical approaches
for multi-component classification. Therefore, Yoo et al. [25] adopted the
Loopy Belief Propagation (LBP) on a multi-component graph consisting of users,
items, and reviews. The algorithm stated different edge potential assumptions
on links between the components in the graph. Yoo et al. [25] then applied the
assumptions in an iterative algorithm called “Supervised Belief Propagation”
(SBP). SBP calculated the final probability of each review being a fraud.

The performance of the SBP showed an AUC of 93% on the Epinions dataset with
131,828 components (users, items, and reviews) and 841,372 edges.

To better utilize the potentials of the different classical supervised
approaches, Kumar et al. [90] proposed hierarchical supervised learning to
detect fraudsters on the Yelp dataset. User-based features such as Review Gap
(RG) and Review Count (RC) were extracted. The extracted features were then
used as the input to different classical classifiers such as LR, K-Nearest
Neighbor (KNN), NB, AdaBoost, RF, and SVM. LR showed an AUC of 72.3% as the
best classifier.

Classical supervised approaches showed a significant potential to handle fraud
detection in different studies. However, such approaches suffer from a
limitation in handling small datasets and require human experts to label the
training dataset. Nevertheless, supervised learning has shown promising
performance in fraud detection.

####  IV-A2 Deep Learning

Since 2016 [51], the fraud detection research community has witnessed the
success of deep learning approaches. Mostly utilized as an end-to-end
architecture, deep learning approaches take texts as the input for a
prediction task of either classification or regression. In other words, deep
learning mostly does not have explicit feature extraction, and instead
combines feature representation learning and the classification step into one
end-to-end network.

To address the domain dependent limitation of classical approaches, Liu et al.
[35] investigated traditional features for fraud review detection on a Chinese
website. Liu et al. [35] employed texts and metadata as multi-modal data to
jointly learn a representation of each review. The architecture is given in
Fig. 9. The joint representation was then fed to a bi-directional Gated
Recurrent Unit (GRU) to learn the review text representation. Behavioral
features such as the Ratio of Positive rating (PR), Burstiness (BST), and
Rating Deviation (RD) were combined with the joint features to form a unique
representation for each review. A CNN was then trained on these features to
predict the class for each review. The dataset was collected from two large
Chinese websites, namely Dianping hotels and restaurants. The results showed
an F1-score of 68% on the collected dataset.

Figure 9: Learning multi-modal embedding representation of the n-gram textual
and rich behavior features [35].

To utilize the potential of both classic and deep learning approaches, Dong et
al. [91] proposed a technique to combine deep learning and traditional
classification. Both user’s behavioral data and text semantic features were
extracted in the feature extraction stage. For the former, the entropy of
scores, review time entropy, the entropy of ratings, and the entropy of the
product’s comment time were extracted and combined with an indicator called
the same date indicator. For the latter, the review length and the review text
semantics were extracted. An AutoEncoder (AE) was applied to reduce the noisy
components from the initial representation. An overview of AE is given in Fig.
10. The bottleneck features were then used as the input to a Neural Decision
Tree (DT); i.e., a decision tree where each node represents a probabilistic
distribution. The approach showed an accuracy of 95.85% on the Amazon dataset.

Figure 10: Proposed neural autoencoder decision forest in [91].

To compare the effectiveness of different deep learning approaches, Hajek et
al.[92] proposed an approach to compare a Deep Feed Forward Neural Network
(DFFNN) with a CNN. Review ratings and word embedding (learnt using skip-gram)
were used as the features. Such features were then fed to a CNN and a DFFNN
for the final classification. The CNN model achieved an accuracy of 81.30% on
the Amazon dataset.

Early studies [51, 35] on deep learning mainly focused on performance
improvement, recent studies [91, 92] focused on different deep learning
techniques to compare and analyze the potential of such approaches in the case
of challenging fraud detection tasks. The state-of-the-art study [35]
demonstrates the combination of deep learning with other aspects (e.g.,
multimodal data) resulted in a significant improvement.

###  IV-B Semi-supervised

Supervised approaches have the limitation of relying upon a quality labelled
dataset by human experts which is expensive to obtain. Semi-supervised
learning provides an opportunity to utilize the existing small set of labels
to propagate the available knowledge to unlabelled samples. Different from
supervised learning, there is no training set to train a model, but the model
progressively learns the trend in data and tunes the hyper-parameters. Given
the effectiveness of deep learning in handling multimodal data, Deng et al.
[33] also proposed a framework with features extracted from both metadata and
the content of the review text. Several features were extracted from the
metadata as behavioral features: User-Level (UL) and User Mobility from the
IPs which were concatenated with the Bag of Word (BOW) as a text-based
feature. The joint representation was then fed to an autoencoder for
dimensionality reduction. K-Nearest-Neighbor (KNN) was applied to the features
to achieve different feature clusters. A new dataset was collected from JD.com
to evaluate the effectiveness of the proposed approach on different domains.
The proposed approach showed an accuracy of 89.3% on the JD dataset. The best
performance was obtained with an accuracy of 83.75% on the TripAdvisor
dataset.

Recently introduced semi-supervised approaches mostly utilized graph-based
techniques to deal with fraud review. Shehnepoor et al. [49] proposed NetSpam,
combining text-based and behavioral features and obtaining the importance of
each feature using a weighting method based on the Metapath concept. The
weights were used in a Heterogeneous Information Network (HIN) to link the
reviews with similar weights. A novel scoring function was then proposed to
calculate the probability of a review being a fraud.

$Pr_{u,v}=1-\prod_{i=1}^{L}1-mp^{p_{i}}_{u,v}\times W_{p_{i}}$ (1)

where $Pr_{u,v}$ is the probability of user $u,v$ being fraudster based on the
metapath value between two users on feature $p_{i}$ ($mp^{p_{i}}_{u,v}$) and
$W_{p_{i}}$ is the weight of the feature. The results on the Yelp dataset
demonstrated an AUC of 77%.

Given the effectiveness of the graph-based techniques, Yilmaz et al. [93]
proposed a semi-supervised approach called SPR2EP. SPR2EP incorporated two
types of features; review-based features, and user-item features. The former
was extracted through an algorithm called Doc2Vec, and the latter was obtained
through an algorithm called Node2Vec. Both approaches relied on Word2Vec to
generate the embeddings. The proposed Doc2vec converted each document to a
vector embedding with a size of 384. Node2Vec mapped the review text to the
same vector embedding space of size 384. The concatenated features were then
fed to a Linear Regression model for the final scoring. The best performance
was obtained with an AUC of 83.18% on the Yelp dataset.

As one of the latest studies, Wang et al. [94] proposed a vote-based
integration model to harness the power of different heterogeneous information
rank-based algorithms. The proposed framework, SpamVote, took ranked lists
generated by different unsupervised detection models as inputs, computed the
weighting of ranked reviews based on the similarity between two ranked lists,
and output the score of a review is a fraud review. SpamVote showed NDCG@100
of 93.68% on the Yelp dataset.

###  IV-C Unsupervised

In addition to their promising performance in semi-supervised models, graph-
based approaches were also employed for unsupervised learning.

Liu et al. proposed HoloScope [95] that models data as a bipartite graph with
users as the source nodes and items as the sink nodes.

TABLE VIII: Studies based on approaches. Approach |  Type |  Model |  Output |  Paper
---|---|---|---|---
Supervised |  Classical |  Support Vector Machine |  Binary classes |  Myle et al. [31]
Bayesian Network |  Ranked scores |  Li et al. [89]
Linear Regression |  Binary classes |  Peng et al. [88]
Linear Regression |  Binary classes |  Kumar et al. [90]
Deep Learning |  Convolutional Neural Network |  Binary classes |  Wang et al.[96]
Convolutional Neural Network |  Binary classes |  Liu et al.[35]
Neural Decision Tree |  Binary classes |  Dong et al. [91]
Convolutional Neural Network |  Binary classes |  Hajek et al. [92]
Semi supervised |  Tabular |  Linear Regression |  Binary classes |  Wang et al. [32]
K-nearest Neighbor |  Clusters |  Deng et al. [33]
Linear Regression |  Binary classes |  Rout et al. [97]
Linear Regression |  Binary classes |  Yilmaz et al. [93]
Voting |  Binary classes |  Wang et al. [94]
Graph |  Heterogeneous Information Network |  Ranked scores |  Shehnepoor et al. [49]
Unsupervised |  Tabular |  Expectation Maximization |  Ranked probabilities |  Xu et al. [74]
Maximum Likelihood Estimation |  Binary classes |  Dong et al. [98]
Ranking |  Ranked scores |  Xu et al. [99]
Graph |  Iterative Mutually Exclusive |  Ranked scores |  Hooi et al. [42]
Bayesian network |  Ranked scores |  Kumar et al. [43]

Nodes were linked through a directed edge signed by either rating or the
timestamp of the review. Holoscope then applied a mutually exclusive iterative
algorithm inspired by [100, 42] to score the users as fraudsters or genuine.
The results on the Yelp dataset demonstrated an AUC of 99.5%.

Hooi et al. [42] also proposed a novel unsupervised approach to deal with a
recent hot-topic challenge in fraud detection: the camouflaged fraudsters.
Camouflage refers to an act of writing genuine reviews by fraudsters to escape
detection. Hooi et al. mapped the components to a bipartite network with items
and reviews as two different node types in the graph. An iterative algorithm,
inspired by [43], was applied to a bipartite network to find the score of a
new user potentially being a fraudster. Results on Amazon showed an accuracy
of 89%. Recently, unsupervised approaches are also employed to address new
challenging topics such as the cold-start problem. Kumar et al. [43] proposed
REV2 to incorporate a Bayesian Belief Network to perform inference on three
components (fairness of the user, the goodness of the product, and the
reliability of a reviewer) from five different datasets including Flipkart,
Bitcoin OTC, Bitcoin Alpha, Epinions, and Amazon. To handle the cold-start
problem, Kumar et al. utilized a Laplacian smoothing. To score a user as a
fraudster or honest, a mutually recursive algorithm was applied to the
components. Results demonstrated an accuracy of 64.89% for the multi-component
classification on the Amazon dataset.

As unsupervised approaches are not limited by the labeling quality of the
reviews, recent research mostly relied on unsupervised methods to address new
challenging topics (such as cold start and camouflage). The recent approaches
mainly employed graph-based techniques to model the components. The potential
of unsupervised approaches is not only to improve the detection accuracy in
fraud detection but also to address the current and future challenges.

###  IV-D Summary

The volume timeline of previous studies on different approaches is provided in
Fig. 11. Similar to Fig. 7, there is a boost in the number of studies focusing
on models as the main contribution in 2018, coupled with the introduction of
deep learning for fraud detection. Thereafter, most studies concentrated on
deep learning for the feature representation learning step. A summary of
approaches is given in Fig. VIII.

Week 020172018201920202021Myle et al.Li et al.Peng et al.Wang et al.Xu et
al.Yoo et al.Hooi et al.Shehnepoor et al.Deng et al.Rout et al.Yilmaz et
al.Kumar et al.Kumar et al.Dong et al.Narayan et al.Hassan et al.Xu et al.Liu
et al.Dong et al.Hajek et al. Figure 11: The volume timeline of the most
recent studies using different approaches for fraud detection. Diameter of
each circle is proportional to the number of studies for one year period
[2017-2018, 2018-2019, etc.]

##  V Analysis

This section includes a detailed analysis of the findings in various
subsections. We provide an analysis of three types of topics: classical,
ongoing, and future challenge. In the first section, we provide an analysis of
classical topics already investigated in previous studies. After that, we
provide an overview of possible future directions. In the subsequent section,
we examine data as the primary challenge, in previous and future studies. We
also suggest future steps to address the shortage of data. Finally, we discuss
different future topics and possible solutions.

###  V-A Discussion on Topics: Classical Challenging Topics

This survey studied 58 recent works with 80% after 2016 (46 out of 58) to
properly present the most recent techniques and challenges in fraud detection.
Fig. 12 shows the research study statistics for different topics: components,
features, and approaches. As Fig. 12 suggests, user and review detection were
the main focus of researchers in recent years (94% combined), while item (as a
separate component) takes up about 6% of the research efforts.

Component(%) Feature(%) Model(%) $0$$20$$40$$60$$80$$100$$100$$100$$100$Item
(C), Text-based (F), Unsupervised (M)User (C), Behavioral (F), Semi-supervised
(M)Review (C), Joint (F), Supervised (M) Figure 12: Percentage breakdown of
studies on components, features and classification approaches.

####  V-A1 Targeted Item Detection

Target item detection benefits fraud detection in different aspects. For
example, it facilitates fraudster group detection, as suggested by Ji et al.
[39]. It has also shown to be effective in dealing with the cold-start problem
[36, 56] by finding items with a burst of reviews written in a short period of
time. This encourages researchers to prioritize target item detection in order
to address potential future challenges.

####  V-A2 Feature Extraction Importance

The uniform distribution over the studies on fraud features (Fig. 12) suggests
an equal importance of each category for researchers. It is worth mentioning
that early studies employed text-based features to address fraud detection. In
recent years, and with the significant advances in deep neural networks, deep
learning techniques were utilized to represent text-based features implicitly
using word embeddings. Deep learning was also utilized as a classifier in the
classification step or in the joint learning task in end-to-end neural
networks. End-to-end approaches provide an opportunity to address fraud
detection, by combining the feature representation and classification step.
Despite behavioral features’ effectiveness in handling fraud review, in recent
years, and with the growth of bot-generated reviews, the behavioral features’
representation may result in an unreliable representation of each component.
However, given that new datasets contain information of the lower layers in
the OSI model (such as IP, MAC address, and other basic information of users),
these will likely compensate for the shortcomings in the application layer
metadata and review text. Joint feature learning is considered to be an active
area of research, since advances in both text-based and behavioral features
result in improved joint representations.

####  V-A3 Graph-based Approach Importance

According to Fig. 12, almost half of the approaches adopted supervised
learning, one-third of the approaches utilized semi-supervised learning and
the rest used unsupervised learning.

Fig. 12 suggests that there is a tendency towards using un/semi-supervised
approaches. More than half of the studies relied on unlabelled data
(unsupervised and semi-supervised approaches combined), while the majority of
the remaining approaches, with supervised learning as their main focus, relied
on the Yelp dataset.

With the lack of data and explanations provided in previous sections, a set of
suitable approaches with a capability to correctly populate the labels for the
unlabelled samples is required. Graph-based approaches, however, empower the
models to utilize the merits of the connection between components to better
generalize the classifications. Conclusively, the findings for the topic
analysis demonstrate that graph-based approaches, as the latest techniques to
address the lack of labels, can be utilized to employ a message forwarding
[34] or inductive learning [42] to populate the labels based on different
criteria. However, such propagation approaches (message forwarding, etc.) are
well explored (Fig. 12) and researchers are using different refinement methods
to deal with the representation tuning.

###  V-B Lack of Data Problem: Ongoing Challenging Topics

The lack of data is the most challenging topic for researchers in fraud
detection. The challenges are across a range of different tasks across all
stages in our systematic fraud detection framework, such as the data
collection for a component, data preparation for feature extraction, and most
importantly the labels used to train or evaluate the framework performance for
the classification/scoring step.

####  V-B1 Labels

The most pressing challenge is the lack of trustworthy ground truth for fraud
reviews, due to the challenging nature of fraud labeling. Due to the
complexity of the task, even human labeling accuracy is no better than a
random classifier [20, 18]. Some platforms (e.g., TripAdvisor) employed review
experts as human judges to label the reviews [50]. Furthermore, the difficulty
of labeling depends on the task. For example, a group labeling task is less
challenging than labeling individual users, since the collaboration among
members of a group provides a reasonable context for comparison and judgement
[39]. Similarly, some platforms use detection algorithms to label the
datasets, e.g., Yelp [69, 41, 93, 96, 36, 56, 86, 79]. Datasets labeled by the
detectors, are called “near ground-truth” datasets, and the labels are mostly
used in semi-supervised or unsupervised approaches [20, 49]. Apart from review
labeling challenges, the other challenge is the lack of ground truth for the
other components, such as users (fraudster or genuine user) and items
(targeted by fraudsters or non-targeted). Some studies label the users with at
least one fraud review as fraudsters [20]. Such labeling would result in
misclassification, given that the labels are provided as near-ground truth.

There is a growing trend towards using datasets populated by web crawling
applications, to deal with the lack of data problems. The Tencent dataset has
been extensively used in recent studies [21, 101], and provided labels as
near-ground truth. Fig. 13 shows that a considerable number of studies use the
Yelp dataset. The first possible explanation is that the Yelp platform is a
public dataset and is accessible for different purposes. The Yelp dataset also
provides the ground truth for reviews based on the platform recommender.
Similarly, the Amazon dataset provides a variety of datasets for different
businesses with near-ground truth labels. TripAdvisor relies on a balanced
distribution of fraud and genuine reviews for hotels in Chicago, providing 800
reviews for each class of fraud and genuine reviews. Such a dataset is either
used in studies with classic classifiers [97, 102, 103] or for data
augmentation to overcome data paucity [50, 67]. The remaining datasets are
crawled using web crawlers, mainly to provide an insight for different fraud
detection topics.

Labeling the review texts requires different levels of knowledge. Similar
studies in related areas might be interesting and inspiring in fraud
detection. Studies on detecting fake news, as another type of spam, employed
fact-checking to improve the accuracy of the labeling [104, 105, 106].
Hierarchical fact checking is employed to find the clues about the origin of
the news. However, accessing that level of information requires specific
permission to the data. Nonetheless, fake news detection accuracy somehow
depends on different datasets presented to the algorithm. In fraud review
detection, on the other hand, acquiring such a relationship between different
reviews in multiple platforms is challenging. Fake news detection, however,
can provide some useful insights into how hierarchical fact checking [107] can
be helpful to provide a degree of confidence on the labeling. Such an
exploration requires considerable efforts on the labeller end.

As another direction, incorporating expertise from other areas such as
psychology could be useful in identifying key implications of written reviews
and the intention of the user through the use of psychological indicators.
Such indicators can be driven from the sentiment analysis of the review text.
The indicators are then correlated with the analysis from a psychological
point of view [108, 109]. Porshnev et al. [108] studied the emotional state of
different users in Twitter using sentiment analysis. Word frequency is also
used as a feature to analyze the psychological state of the people who
experienced a sudden change in the isolation with the COVID-19 outbreak. The
outcome of such psychological indications can be used to help the labeller.

Given all the explanations and directions, achieving a large amount of data
requires considerable investment from companies. Marciano et al. [110]
reported that fraud reviews in online shops cost $152 billion a year. Facebook
introduced new machine learning techniques to address scam on Facebook in 2018
[111]. Although such contents are considered as opinion spam, finding the
accurate label for the task requires significant investment to provide both
budget and data to empower the labeling tools for finding the correct labels.

####  V-B2 Multi-modal Data

Fortunately, several platforms (e.g., Yelp, Amazon, and TripAdvisor) provide
datasets including the review text and the metadata (e.g., user ID, item ID,
review ID, date of a written review, rating given by the review, and the
label) for each review. However, the platforms also need to withhold important
information to secure the users’ privacy.

YelpAmazonTripAdvisorTencentResellerRatingMTDianpingEpinionsWeiDaiLendingTRECExpediaGooglePricelineWeiboJDSWM$0$$5$$10$$15$$20$$20$$10$$8$$2$$2$$2$$2$$2$$1$$1$$1$$1$$1$$1$$1$$1$$1$Number
of studies Figure 13: Dataset distribution over different studies. Note that
the most recent study on each dataset is provided alongside the dataset.

3) More specifically, Yelp, which has been the most studied dataset (as shown
in Fig. 13) includes the necessary metadata such as user ID, item ID, date of
the written review, and the rating given by the review that can be used to
extract behavioral features for user activity at the application layer of the
OSI model [112]. However, experienced fraudsters employ a camouflage strategy
to simply cover up their behavioral footprints and manage their traceable
metadata. Although different approaches were proposed to address the
camouflage problem, the performance of the state-of-the-art approaches is
limited due to the lack of information of the user behavior at the lower layer
of the OSI model (e.g., network layer). To deal with the camouflage problem
more information is required to help the detection algorithm to fully trace
the user’s behavior. Unfortunately, the well-known platforms are only
providing particular confidential information such as the user IPs in each
session, location coordinates, etc. It is worth mentioning that some platforms
provide privileges (e.g. APIs) for the crawlers to collect data from different
data modalities, also referred to as Multi-modal data [63]. Gathering
information from different modalities for feature extraction is an unexplored
and a potential topic to consider for future studies [63]. Similarly, various
studies employed multi-modal data to address spam tweets [61, 62] on different
domains of the social review platforms. Using different data models, e.g., the
Denial of Service (DoS) containing different attributes, is an effective
approach in detecting fraud reviews. Liu et al. [35] suggest that feature
types can be regarded as different data modalities. The features are likely
extracted from one dataset with the metadata as the attribute to then extract
text-based and behavioral features as different modalities. Attributes from
the network layer (e.g., IP of the user) were utilized in a few studies [21,
101] to initialize a new area of research in fraud detection. Hence, future
studies can extract multi-modal data and combine such features to achieve a
better behavioral representation of each user.

For example, Wen et al. [101] and Wang et al. [21] collected data from the
Tencent platform which includes IP and different physical layers alongside
information from the application layer. Technically speaking, such multimodal
data enables the detection model to learn a joint representation and then
extract a unique vector for each user. The vector not only represents the
traceable user’s behavior at the application layer but also the background
activities of the users. Such additional information help the detector with
new fraudster detection in a shorter time compared to a situation in where the
detector is provided with the information from only one data modality, and
this can also address the cold-start problem [36].

However, the inconsistency between the different modalities of data could also
be an interesting topic to explore. For example, different studies considered
rating as a sentiment indicator, representing the tendency of the user towards
the item. Surprisingly, with the sentiment analysis of a text, such
assumptions are more likely to be inaccurate [36, 34], due to the
inconsistency between the rating and the real sentiment of the review. Hence,
to obtain accurate semantics, studies should solely rely on the sentiment
analysis of the review text (and not rating). To train a joint representation
of the text and metadata, several objective functions are proposed in
different studies [36, 67].

####  V-B3 Transfer Learning

One of the most recent topics is to use transfer learning to improve fraud
detection accuracy. Gupta et al. [113] employed BERT and transferred an NLP
task to fraud review detection with different configurations. However, the
study employed transfer learning as part of a representation extraction step
from the text, though transfer learning can be applied to different steps of a
machine learning task. To be specific, in a relatively similar area, such as
speech recognition, studies applied transfer learning to improve the accuracy
of the children’s speech recognition using a model already trained with adult
data [114, 115]. The transfer learning approach has shown to be useful in the
case of data scarcity, as with a model trained with plentiful data on the
other task, replacing the classification layer and initial training of the
layer which requires a relatively smaller set of data [116].

###  V-C Hot Topics: Future Challenging Topics

There are some recent challenging topics addressed only by a few studies. We
discuss such topics in the following and provide insights on possible
solutions.

####  V-C1 Bot Generated Review Detection

Bot review generation was introduced by Yuanshun et al. [18] and then explored
by different studies [50, 67]. Yuanshun et al. [18] claimed that bot-generated
reviews are indistinguishable from human-written reviews from different
aspects. Such reviews are manipulated by fraudsters, and generated in large
volume, with minimum cost. A generator can be trained to generate authentic
reviews, leaving neither human traces nor footprints. Consequently, behavioral
features become less useful for bot-generated review detection. Yao et al.
[117] reported an important observation on a bot trained by a character-based
language model over the whole corpus. The study discussed one way to detect
such fraud reviews is to parameterize the character distribution, as the
distribution is one key factor in determining a bot review. Relying on a
specific distribution, the study detected the bot-generated review using a
likelihood measurement. This can be applied to different platforms (Yelp,
TripAdvisor, etc.) using different criteria, as each domain has its own
specific terminology.

Shehnepoor et al. [67] proposed ScoreGAN with synthetic reviews generated by a
Generative Adversarial Network, and the performance of a model trained on the
Yelp dataset is improved. Shehnepoor et al. thus suggested that there is a
high probability of bot review presence in the Yelp dataset. A closer look at
the generated review set reveals that generated reviews are often short. This
is because generating long semantically coherent reviews requires a large
training dataset. The syntax is not an issue, as there are only a relatively
small set of syntax rules. A possible future direction is to distinguish
between human-written fraud reviews and bot-generated fraud reviews. This will
provide useful hints to characterize both human and bot-generated reviews. Few
studies investigated such a problem and the studies mostly suffered from
uncertainties in determining whether the final results can be validated unless
the content is synthetically generated. Shehnepoor et al. also discussed the
importance of using the rating in combination with the review text, as the
only behavioral indicator of bots.

In summary, these findings suggest that bots can be identified with three key
factors: first, certain distribution over characters, or words; whether
synthetically correct, but semantically not; and finally, coherence between
text and rating.

####  V-C2 Cold-start Problem

The cold start problem can be expanded to a multi-domain (hotels, restaurants,
online shops, etc.) problem, where different domains are involved in tracking
the behavior of a user. A fraudster typically does not only write reviews on
one platform, but multiple ones to maximize the impression. Similar studies
are conducted in recent years to find the path of influential users in
different social networks, namely multi-layer networks [118, 119]. As
previously mentioned, fake news detection studies often employ a similar
approach to verify the accuracy of a news article. In a similar study,
Sivasankari et al. [120] used a multi-domain series of data to trace back the
truth about suspicious news. Such cross-domain behavior analysis with a focus
on similar patterns can be useful [56]. Although You et al. [56] employed a
similar approach towards the cold-start problem, the study lacks the
behavioral pattern exploration and analysis and solely relies on extracting
attributes from multiple domains.

Graph-based approaches [22] are potentially suitable for the
classification/scoring step to train the joint representation and address
different challenges (e.g., cold-start problem). Graph-based learning
considers the relations between the extracted features to refine the prior
knowledge using a network-based inference algorithm. The inference algorithms
mainly embed the network-based representations of the components into a single
representation. Some previous graph-based approaches (e.g., Loopy Belief
Propagation [34, 20]) suffered from limitations in normalizing the nodes’
representation. The limitation leads to incorrect component predictions, since
the feature aggregation is simply altered once the node’s degree is changed.
This allows fraudsters to manipulate their behaviors through camouflage. One
possible solution is to employ graph-based inductive learning [121]. Inductive
learning facilitates the representation learning for single or multiple
components in a graph. The representations can then be used to find the
primary representations of the newly introduced nodes, such as users or items
in a social platform to handle the cold-start problem.

In summary, a potentially more optimized solution is to combine 1) user path
prediction over a multi-layer network and 2) using the cross-domain features
(as suggested by [56]) to find similar co-occurring behavioral patterns, or 3)
graph-based approach to propagate the representation to unseen data.

years$2015$$2017$$2019$$2021$$2023$Cold-start introduced by Wang et al.
[36]Bot review generation introduced by Yuanshun et al. [18]Multi-modal
feature extraction introduced by Liu et al. [35] Figure 14: Emerging areas
timeline and potential future work.

We summarized the future works in a timeline depicted in Fig. 14, highlighting
the three current hot topics for fraud detection. We picked an open problem in
each of the three stages in our proposed systematic framework (component
selection, feature extraction, classification/scoring) and elaborated on the
current study. Bot-generated review detection was introduced as an open
problem in the component selection step by Yuanshun et al. [18] and was
primarily investigated by two studies by Aghakhani et al. [50] and Shehnepoor
et al. [67]. As explained in the feature extraction step, the main challenge
in the feature extraction step is to find a suitable representation for the
selected component. Multi-modal data can effectively address such a challenge,
as introduced by Liu et al. [35]. Extracting data from different network
layers of the OSI model [21, 101] results in better activity modeling of
reviewers in a social review platform, and thus provides a more informative
feature representation. Graph-based approaches can be treated as a scoring
method to address different challenges such as the cold-start problem
(introduced by Wang et al. [36]) through its capability in modeling complex
relationships between the components.

##  VI Conclusion

With an increase in the popularity of social review platforms for businesses
and their customers, the traditional marketing media (e.g., broadcast and
print) are losing ground to such web and mobile-based social review platforms.
Fraudsters seize such opportunities for their benefits and take advantage of
the media to write fraud reviews on targeted businesses. Characterizing fraud
reviews provides an opportunity for new researchers to better understand the
nature of such reviews, and hence improve the detection algorithms’
performance. Several studies provided an overview to define fraud and examined
the detection algorithms from different perspectives [19, 13, 40]. However,
researchers may still be confused about the overall fraud detection framework.
In this study, we proposed a systematic framework for fraud review detection
with three stages: component selection, feature extraction and representation,
and classification/scoring.

  * •

In the component selection step, all studies considered either review, user,
or item as their selected component to explore. While primary studies focused
on fraud review detection, user group detection has recently attracted the
attention of researchers, but it is still far from being fully resolved. Bot
generation is another challenging topic due to the capability of deep
learning-based review generation. Targeted item detection is also a potential
topic to consider, as it is not investigated as a separate component and has
always been studied alongside user and review for multi-component
classification.

  * •

We analyzed feature extraction across three different categories; text-based,
behavioral, and joint features. With recent progress in deep learning, new
word embedding techniques have been employed to extract features and address
the limitations of lingo-statistic text-based features. Behavioral features
were also investigated individually or in a combination with text-based
features as joint features.

  * •

Classification/Scoring techniques were categorized based on the approaches
used to deal with the lack of reliable data. Semi-supervised and unsupervised
learning deals with partial labeling data for detection, while deep learning
can suggest a considerable classification improvement over classic approaches.
Data augmentation approaches such as GANs are a possible solution to address
data paucity.

In addition to the technical challenges, new problems such as cold-start, bot
fraud generation, and user group detection are becoming increasingly
important. One possible direction for user group detection is to use graph-
based solutions (e.g., Graph Convolutional Network) due to their effectiveness
in realizing the pair-wise tightness of members of a group. Multi-modal data
can also be considered as an important future work since it provides a
comprehensive representation of all the different OSI model layers associated
with an online social review platform to describe a component. To conclude, in
this study, we introduced a systematic framework for fraud detection tasks.
The techniques outlined for each step showed improvements in fraud detection.
It is hoped that this review will encourage researchers to deepen their
understanding of fraud detection and provide solutions to emerging challenges.

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