A systematic literature review on spam

content detection and classification

Sanaa Kaddoura1, Ganesh Chandrasekaran2, Daniela Elena Popescu3
and Jude Hemanth Duraisamy4
1
Zayed University, Abu Dhabi, United Arab Emirates
2
Electronics and Communication Engineering, Sri Eshwar College of Engineering, Coimbatore,
Tamil Nadu, India
3
Faculty of Electrical Engineering and Information Technology, University of Oradea, Oradea,
Romania
4
Electronics and Communication Engineering, Karunya Institute of Technology and Sciences,
Coimbatore, Tamil Nadu, India

ABSTRACT
The presence of spam content in social media is tremendously increasing, and
therefore the detection of spam has become vital. The spam contents increase as people
extensively use social media, i.e., Facebook, Twitter, YouTube, and E-mail. The time
spent by people using social media is overgrowing, especially in the time of the
pandemic. Users get a lot of text messages through social media, and they cannot
recognize the spam content in these messages. Spam messages contain malicious links,
apps, fake accounts, fake news, reviews, rumors, etc. To improve social media security,
the detection and control of spam text are essential. This paper presents a detailed
survey on the latest developments in spam text detection and classification in social
media. The various techniques involved in spam detection and classification involving
Machine Learning, Deep Learning, and text-based approaches are discussed in this
paper. We also present the challenges encountered in the identification of spam with its
control mechanisms and datasets used in existing works involving spam detection.

Subjects Computational Linguistics, Data Mining and Machine Learning, Natural Language and
Speech, Network Science and Online Social Networks
Keywords Spam Content, Machine learning, Deep learning, Natural language processing, Social
Submitted 5 November 2021 media analysis, Classification, Text mining, Data mining
Accepted 6 December 2021
Published 20 January 2022
Corresponding author
INTRODUCTION
Jude Hemanth Duraisamy, The word spam generally means some unwanted text sent or received through social media
judehemanth@karunya.edu sites such as Facebook, Twitter, YouTube, e-mail, etc. It is generated by spammers to divert
Academic editor the attention of the users of social media for the purpose of marketing and spreading
Vimal Shanmuganathan
some malware etc. The e-mail spam messages are sent in bulk to various users, with the
Additional Information and
intention of tricking them into clicking on fake advertisements and spreading malware on
Declarations can be found on
page 21 their devices. The spam messages provide a good source of income for the spammers
DOI 10.7717/peerj-cs.830 (Bauer, 2018) and, hence, they continue to spread them rapidly. To combat spam in e-mail,
Copyright a lot of techniques have been involved, but the spam content continues to increase
2022 Kaddoura et al. (Statista, 2017). These spam messages cause financial loss to business e-mail consumers
Distributed under and also to the general users of e-mail (Okunade, 2017).
Creative Commons CC-BY 4.0 Spam is common on social media sites like YouTube, and it mainly consists of
comments and links to pornographic websites, as well as irrelevant videos. These

How to cite this article Kaddoura S, Chandrasekaran G, Elena Popescu D, Duraisamy JH. 2022. A systematic literature review on spam
content detection and classification. PeerJ Comput. Sci. 8:e830 DOI 10.7717/peerj-cs.830
 comments are sometimes created automatically by bots. Although the definition of
spam on online video game sharing services is debatable, instances of message flooding,
requests to join a specific group, violations of copyrights, and so on are occasionally
referred to as spam. Spam in blogs, often known as splog, refers to comments that have
nothing to do with the topic of discussion. Frequently, these comments are accompanied
by links to commercial websites. Some splogs are devoid of unique content and contain
stuff plagiarized from other websites (Rouse, 2015).
Spam is also included in written reviews of products that are available on social
networking sites. According to Liu & Pang (2018), about 30–35% of online reviews are
deemed spam. These spam reviews are intended to influence people’s purchasing decisions
and to affect product ratings (Saini, Saumya & Singh, 2017; Ho-Dac, Carson & Moore,
2013). As a result, detecting bogus reviews appears to be a major worry, and online review
systems may become utterly useless unless this vital issue is addressed (Jin et al., 2011;
Govtnaukries, http://www.govtnaukries.com/you-wont-ever-use-head-and-shoulder-
shampoo-after-watching-this-video-facebook-spam/). Fake/spam profiles abound on
social networking platforms like Facebook and Twitter, and users are bombarded with
SMS messages from these identities. To analyze the spam content many researchers Song,
Lee & Kim (2011) have employed the attributes from Facebook including community,
URL, videos and Images. By identifying and filtering the spam and non-spam accounts
Stringhini, Kruegel & Vigna (2010) could identify and characterize the spam using
statistical techniques. Mateen et al. (2017) have used honey-profiles to record the activity of
the spammers and applied this technique to social media content for spam detection using
a novel tool. The graph models were also popular to detect spam based on the different
features of the map and they could find the relationships that exist among the social media
users (Benevenuto et al., 2010). In recent times, the machine learning algorithms are
getting popular and they are used in spam detection (Rathore, Loia & Park, 2018; Liu et al.,
2016; Zheng et al., 2016; Serrano-Guerrero et al., 2015).
The steps in detecting spam on social media are often as follows. Obtaining the spam
text collection (dataset) is the initial step. Because these datasets frequently have
unstructured text and may contain noisy data, preprocessing is almost always necessary.
The following step is to select a feature extraction method, such as Word2Vec, n-grams,
TF-IDF, and so on. Finally, a variety of spam detection technologies, such as machine
learning, deep learning, and Lexicon-based algorithms, are utilized to decide whether texts
are spam.
The rationale of our work is to bring out a detailed survey of several spam detection and
categorization algorithms. We are aware that many previous surveys on spam detection
may not have acquired the information that we obtained from various popular academic
data sources. Some previous efforts on spam identification from social media have
constrained themselves to only a few limited academic sources. Some earlier studies failed
to highlight the benefits and drawbacks of various spam detection and classification
systems. The novelty of our work is that we used data from a variety of reputable academic

Kaddoura et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.830 2/28

 sources to achieve our goal of identifying spam content on social media. We have also
highlighted certain significant strategies, along with their benefits and drawbacks when
applied to various spam datasets. We also covered deep learning and other crucial Artificial
Intelligence (AI)-based spam detection approaches that have previously only been found
in restricted investigations.
This extensive survey will assist academics who are interested in spotting social media
spam using AI techniques, as well as addressing the issues associated with it. Using the
proposed survey, researchers will be able to select optimal detection and control
mechanisms for spam eradication. Our work will let academics compare the many existing
spam detection works in terms of their merits, limits, approaches, and datasets employed.
This study will also assist researchers in addressing current research possibilities, concerns,
and challenges connected to spam text feature extraction and classification, as well as
specifics on various data sets used by other researchers for spam text detection.
We compare the accuracy of existing spam text detection systems in order to
determine which ones are the most effective. “Survey Methodology” describes the survey
methodology used to conduct our comprehensive review. “Steps for Detecting Spam in
Social Media Text” uses a block diagram to explain the multiple steps involved in spam
detection. “Collection of Social Media Textual Data (Dataset Collection)” provides a
summary of the datasets available for social media spam text. The following section,
“Pre-processing of Textual Data”, goes over the various spam text pre-processing
procedures. “Feature-Extraction Techniques” and “Spam Text Classification Techniques”
investigate several feature extraction methodologies and spam categorization algorithms.
Deep learning techniques for spam classification are discussed in “Deep Learning (DL)
Approaches for Spam Classification”. “Challenges in Spam Detection/classification from
Social Media Content” discusses the difficulties encountered in spam detection, and “Open
Issues and Future Directions” concludes with a list of references.

SURVEY METHODOLOGY
The goal of this survey is to undertake a thorough literature evaluation on approaches
for detecting and classifying spam content in social media. There are several sources of
textual data on social media platforms such as Facebook, Twitter, E-mail, and YouTube.
A variety of ways have been used to detect and regulate spam text. Our efforts are
primarily motivated by a desire to learn more about different spam text detection and
categorization algorithms. This section discusses the survey methodology that we used to
conduct our detailed spam detection review.

Selection of keywords and data sources

Based on our research objective, the initial search keywords were carefully chosen.
Following an initial search, new words discovered in several related articles were used to
generate several keywords. These keywords were later trimmed to fit the research’s
objectives. We chose certain search keywords based on the goal of our survey work, and
after performing an initial search on those words, several keywords were derived from

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 Table 1 Description about academic databases and their links.
Academic Data sources Search string Links
WoS Social spam https://apps.webofknowledge.com/
Scopus Spam AND Twitter https://www.scopus.com/
Springer Spam AND Artificial Intelligence https://link.springer.com/
IEEE Xplore Social spam AND Artificial Intelligence https://ieeexplore.ieee.org/
ACM Digital Library Online spam AND Review Spam http://dl.acm.org/
Science Direct Social media AND Spam http://www.sciencedirect.com/

selected articles. The number of keywords is then reduced in order to meet our research
goal.

Database selection
We extracted research papers from a few academic digital sources to conduct the literature
review. Expert advice was sought regarding source selection, and databases such as Web of
Science (WoS), Scopus, Springer, IEEE Xplore, and ACM digital library were used to
collect research papers for our study. We used search query terms such as “social media
spam,” “twitter spam,” “review spam,” and “spam text,” among others. The academic
data sources with their links that are used in our work is listed in the Table 1 below.
In this review, the title of each paper was scanned and identified for possible relevance to
this review. Any paper that does not refer to social media spam was eliminated from
further investigation. The abstract and keywords of the publications were scanned for a
deeper review and a better understanding of the papers. The Fig. 1 below displays the
distribution of articles depending on publishing types such as journals, conference
proceedings, books, and other reference materials that were referred for our extensive
spam detection survey.
We may conclude from the article distribution pie-chart that for our work, the majority
of the articles referred to were from journals and conference proceedings, and that some
technical reports were also used to obtain material for our systematic literature review.

STEPS FOR DETECTING SPAM IN SOCIAL MEDIA TEXT

The task of spam detection and classification requires several processes, as depicted in
Fig. 2. Data is collected in the first stage from social networking sites such as Twitter,
Facebook, e-mail, and online review sites. Following data collecting, the pre-processing
activity begins, which employs several Natural Language Processing (NLP) approaches to
remove the unwanted/redundant data. The third phase entails extracting features from the
text data using approaches such as Term Frequency-Inverse Document Frequency
(TF-IDF), N-grams, and Word embedding. These feature extraction/encoding approaches
convert words/text into a numerical vector that can be used for classification.
The last step is the spam detection phase, which employs several Machine Learning
(ML) and Deep Learning techniques to classify the text into categories like spam and non-
spam (ham).

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 Figure 1 Articles distribution based on publication type.
Full-size  DOI: 10.7717/peerj-cs.830/fig-1

Figure 2 Steps in spam detection. Full-size  DOI: 10.7717/peerj-cs.830/fig-2

Kaddoura et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.830 5/28

 Table 2 E-mail spam datasets with their description.
S. No Dataset name Description Reference Web link
1 Spam Assassin 1,897 spam and 4,150 ham messages (Méndez et al., 2006) https://spamassassin.apache.org/old/
publiccorpus/
2 Princeton Spam Image 1,071 spam images (Biggio et al., 2011) https://www.cs.princeton.edu/cass/spam/
Benchmark
3 Dredze Image Spam Dataset 3,927 spam and 2,006 spam images (Almeida & Yamakami, https://www.cs.jhu.edu/~mdredze/datasets/
2012) image_spam/
4 ZH1–Chinese email spam 1,205 spam and 428 ham text emails (Zhang, Zhu & Yao, https://archive.ics.uci.edu/ml/datasets/
dataset 2004) spambase
5 Enron-Spam 13,496 spam and 16,545 non spam (Koprinska et al., 2007) http://www2.aueb.gr/users/ion/data/enron-
email text spam/

COLLECTION OF SOCIAL MEDIA TEXTUAL DATA (DATASET

COLLECTION)
The first phase in spam identification is the collecting of textual data, comprising spam and
non-spam (ham) material, from social media sites such as Twitter, Facebook, online
reviews, hotel evaluations, and e-mails. They are extracted with the help of an appropriate
API, such as the Facebook API or the Twitter API, which are both free and allow users to
search and collect data from several accounts. They also enable the capture of data
using a “hashtag” or “keyword,” as well as the collecting of data posted over time. Based on
the text content, we can identify data as spam or ham, and official social networking
sites may flag some accounts or postings as spam. The following Table 2 presents some of
the datasets regarding E-mail spam and Twitter spams. It also displays a description of
the dataset as well as some of the reference studies performed on those datasets.
Twitter, a prominent microblogging network, has attracted people from all around the
world looking to express themselves through multimedia content. Spammers transmit
uninvited information, including malware URLs and popular hashtags. Twitter suspends
accounts that send a high volume of friend requests to people they don’t know, as well as
accounts with a high number of followers but few followers. Table 3 below includes
descriptions and references for some of the Twitter spam datasets.
Sites such as TripAdvisor, Amazon, and Yelp, among others, have online reviews of a
product, hotel, or movie. These reviews include input from previous customers who
have purchased a product or stayed at a hotel. Spammers blend spam content with these
reviews to convey a negative impression about a product or service, causing the firm
financial harm. Table 4 below covers a few datasets linked to online reviews, as well as
several reference studies on detecting spam in reviews.
Table 5 below contains some of the most prevalent spam words seen in e-mail, Twitter,
and Facebook posts. If your e-mail contains any of these words, it’s quite likely that it'll end
up in the spam bin.

PRE-PROCESSING OF TEXTUAL DATA

Text-preprocessing is a significant technique for cleaning the raw data in a dataset, and it is
the first and most important stage in removing extraneous text (Albalawi, Buckley &

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 Table 3 Twitter spam datasets with their description.
S. No Dataset name Description Reference Web link
1 Bzzfeednews dataset 11,000 labeled users, 1,000 spammers and (Mohale & Leung, https://data.world/buzzfeednews
10,000 non-spammer users 2018)
2 Dataset1: Buzzfeed Election Fake election news dataset with 36 real and (Horne & Adalı, https://data.world/buzzfeednews https://
Dataset 35 fake news stories 2017) data.world/datasets/politics
Dataset2: 75 fake news stories
Political news Dataset
3 Twitter ground labeled 6.5 million spam and 6 million non-spam (Chen et al., 2015) http://nsclab.org/nsclab/resources/
ground truth dataset tweets
4 Twitter social honeypot 22,223 spammers and 19,276 non-spammer (Lee, Caverlee & http://infolab.tamu.edu/data/
dataset users Webb, 2010)
5 Stanford Twitter sentiment 1.6 million tweets for spam detection with a (Mazikua et al., http://help.sentiment140.com/for-students
140 dataset total tweet id of 4435. 2020)

Table 4 Spam review datasets with their description.

S. No Dataset name Description Reference Web link
1 Single Domain 1,600 hotel reviews (800 spam and ham) from TripAdvisor (Ott, Cardie & https://github.com/Diego999/HotelRec
hotel review website belonging to 20 popular hotels in Chicago Hancock,
2013)
2 Multi-Domain Hotels, Restaurant and Doctors reviews dataset (2,840 reviews) (Li et al., https://www.cs.jhu.edu/~mdredze/
review dataset 2014) datasets/sentiment/
3 Yelp Review 85 hotels and 130 restaurant reviews in and around Chicago (Mukherjee http://odds.cs.stonybrook.edu/yelpzip-
Dataset et al., 2013) dataset/
4 Store Review 4,08,470 reviews on 14,651 stores obtained from (Wang et al., https://www.kaggle.com/mmmarchetti/
Dataset www.resellerratings.com 2011) play-store-sentiment-analysis-of-user-
reviews/data
5 Amazon e- 40,000 samples for training and 10,000 samples for testing were (Salminen https://data.world/datasets/amazon
commerce collected on various categories like Beauty, Fashion and et al., 2022)
Dataset Automotive etc.
6 Hotel reviews 42 fake and 40 hotel reviews (Yoo & https://www.cs.cmu.edu/~jiweil/html/
dataset Gretzel, hotel-review.html
2009)
7 Trustpilot 9,000 fake and real reviews from online company Trustpilot (Sandulescu & https://business.trustpilot.com/features/
company Ester, 2015) analyze-reviews
review dataset.

Table 5 Most often used spam terms in e-mail, Facebook, and Twitter.
S. No Social Words
network
1 E-mail Full refund, Get it Now, Order now, Order status, Make money, Earn extra cash,
100% free, Apply now, Click here, Sign up free, Winner, Lose weight, Lifetime, Gift
certificate.
2 Twitter Amazing, Hear, Watch, Hunt, Win, ipad
3 Facebook Money, Marketing, Mobi, Free

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 Figure 3 Various text-preprocessing techniques. Full-size  DOI: 10.7717/peerj-cs.830/fig-3

Table 6 Illustration of a sentence and its generated tokens.

Sentence Tokens
“I went to the library to read books” “I”, “went”,”to”,”the”,”library”,”to”,”read”,”books”

Nikolov, 2021; HaCohen-Kerner, Miller & Yigal, 2020). Before extracting features from
text, it is necessary to eliminate any undesired data from the dataset. Unwanted data in
the text dataset include punctuation, http links, special characters, and stop words.
As illustrated in the Fig. 3, there are numerous text-preprocessing techniques available
that can be used to remove superfluous information from incoming text input.

Tokenization
It entails breaking down words into little components known as tokens. HTML tags,
punctuation marks, and other undesirable symbols, for example, are removed from the
text. The most widely used tokenization method is whitespace tokenization. The entire text
is broken down into words during this procedure by removing whitespaces. To split
the text into tokens, a well-known Python module known as “regular expressions” can be
used, and it is frequently used to do Natural Language Processing (NLP) tasks. The
following Table 6 depicts an example of a statement and its tokens.

Stemming
It is concerned with the process of reducing words to their fundamental meanings; for
instance, the terms drunk, drink, and drank are reduced to their root, drink. Stemming can
produce non-meaningful terms that aren’t in the dictionary, and it can be accomplished
using the Natural Language Tool Kit library in conjunction with PorterStemmer.
Overstemming occurs when a significantly more chunk of a word is cut off than is
required, resulting in words being incorrectly reduced to the same root word. Due to
understemming, some words may be mistakenly reduced to more than one root word.

Lemmatization
It employs lexical and morphological analysis, as well as a proper lexicon or dictionary, to
link a term to its origin. The underlying word is known as a ‘Lemma,’ and words such as
plays, playing, and played are all distinct variants of the word ‘play.’ So ‘play’ is the
root word or ‘Lemma’ of all these words. The WordNet Lemmatizer is a Python Natural

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 Table 7 Existing research on spam text pre-processing.
S.No Authors Pre-Processing technique Dataset Classifier Result
used
1 Méndez et al. Tokenization, e-mail text corpora Support Vector Machine Classification accuracy is
(2005) Stemming and Stopwords (SVM) improved with pre-processing
removal
2 Ruskanda Stemming, Lemmatization, Ling-spam corpus dataset with Naïve Bayes (NB) and Pre-processing with NB gives
(2019) Stopwords removal and a total of 962 spam and ham Support Vector better results than SVM
noise removal messages Machine (SVM)
3 Klassen (2013) Data Normalization and Twitter dataset SVM, Neural Networks Overall classification rate of
discretization methods (NN) and Random 84.30% is obtained
Forests (RF)
4 Jain et al. Tokenization and 1.5 million posts from real time NB, SVM and RF RF classifier outperformed the
(2018) Segmentation Facebook data classifiers others with a F-measure of
5 Ahmad, Rafie Stemming and Stopwords Honeypot dataset with 2 Multilayer Perceptron SVM outperformed others with a
& Ghorabie removal million spam and non-spam (MLP), NB and RF precision of 0.98 and an
(2021) tweets accuracy of 0.96

Language Tool Kit (NLTK) module that searches the WordNet Database for Lemmas.
While lemmatizing, you must describe the context in which you want to lemmatize.

Normalization
It is the process of reducing the number of distinct tokens in a text by reducing a term to its
simplest version. It aids in text cleaning by removing extraneous information. By using a
text normalization strategy for Tweets, Satapathy et al. (2017) were able to improve
sentiment categorization accuracy by 4%.

Stopwords removal
They are a category of frequently used terms in a language that have little significance. By
removing these terms, we will be able to focus more on the vital facts. Stop words like “a,”
“the,” “an,” and “so” are frequently used, and by deleting them, we may drastically
reduce the dataset size. They can be successfully erased with the NLTK python library.
Table 7 outlines some of the existing works on text spam detection that use various pre-
processing techniques.
The descriptions and web URLs for some of the libraries or packages available for pre-
processing text data are provided in Table 8 below.
For text pre-processing, researchers in the field of NLP use several methods provided in
the NLTK package. They are open source which are simple to implement and they can also
be used to execute other NLP-related applications.

FEATURE-EXTRACTION TECHNIQUES
Because many machine learning algorithms rely on numerical data rather than text, it is
required to convert the text input into numerical vectors. This method’s goal is to extract
meaningful information from a text that describes essential aspects of it.

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 Table 8 Tools available for pre-processing of spam text.
Library/Package Description Link
TextBlob TextBlob is a Python text processing package. It provides a straightforward API for typical https://textblob.readthedocs.io/
NLP tasks such as part-of-speech tagging and sentiment analysis. en/dev/
Spacy Spacy is a Python Natural Language Processing (NLP) package with a number of built-in https://spacy.io/
features
NLTK The Natural Language Toolkit, or NLTK for short, is a Python-based set of tools and https://www.nltk.org/
programmes for performing natural language processing.
RapidMiner Accessing and analysing various types of data, both organised and unstructured, is simplified. https://rapidminer.com/
products/studio/feature-list/
Memory-Based Can determine the grammatical structure of a sentence by parsing a string of letters or words https://pypi.org/project/MBSP-
Shallow Parser using python for-Python/

Table 9 A bag of words illustration (BoW).

Words Doc-1 Doc-2 Doc-3 Doc-4
Sentiment 2 3 2
Processing 2 4 1
Classification 1 2
Algorithm 1 3 4

Bag of words (BoW)

The bag of words strategy is the most common and straightforward of all feature extraction
procedures; it generates a word presence feature set from all of an instance's words.
Each document is viewed as a collection or bag that contains all of the words. We may
obtain a vector form that tells us the frequency of each word in a document, as well as
repeated words in our document. Barushka & Hajek (2019) developed a spam review
detection model that uses n-grams and the skip-gram word embedding method. They
employed deep learning models to detect spam in 400 positive and negative hotel
reviews from the TripAdvisor website. Table 8 (Term-document matrix) depicts the link
between a document and its terms. The frequency of occurrence of a term in a group of
documents is represented by each value in the Table 9.

N-grams
N-grams, which are continuous sequences of words or tokens in a document, are used in
many Natural Language Processing (NLP) activities. They are classified into several
types based on the values of ‘n,’ including Unigram (n = 1), Bigram (n = 2), and Trigram
(n = 3). Kanaris, Kanaris & Stamatatos (2006) extracted n-gram characteristics from
text using a dataset of 2,893 e-mails. They employed performance factors such as spam
recall and precision in their study. They were able to construct a spam filtering approach
with a precision score of more than 0.90 for spam identification by combining Support
Vector Machine (SVM) with n-grams. They were able to construct a spam filtering
approach with a precision score of more than 0.90 for spam identification by combining

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 Table 10 An N-grams illustration.
S. No Type of N-Gram Example
1 Unigram “I”, “Like”, “to”, “Play”, “Cricket”
2 Bi-gram I Like, Like to, Play Cricket
3 Tri-gram I Like to, to Play Cricket

Support Vector Machine (SVM) with n-grams. Çıltık & Güngör (2008) proposed an
efficient e-mail spam filtering technique to reduce time complexity, and they discovered
that utilizing n = 50 for first n-words heuristics yielded improved results. The words in
Table 10 below are instances of N-grams.

Term frequency-inverse document frequency (TF-IDF)

When employing bag of words, the terms with the highest frequency become dominant in
the data. Domain-specific terms with lower scores may be eliminated or ignored as a result
of this issue. This technique is performed by multiplying the number of times a word
appears in a document (Term-Frequency-TF) by the term’s inverse document
frequency (Inverse-Document Frequency-IDF) across a collection of documents. These
scores can be used to highlight unique terms in a document or words that indicate crucial
information. The computed TF-IDF score can then be fed into machine learning
algorithms such as Support Vector Machines, which substantially improve the results of
simpler methods such as Bag-of-Words. The values of TF and IDF is calculated as per the
following Eqs. (1) and (2)
number of times in a document the word ðwÞ appears
Tf ðwÞ ¼ (1)
total count of words in a document
Total count of documents
Idf ðwÞ ¼ Log (2)
Number of documents that contain the word w

The Fattahi & Mejri (2020) examined the Bag of Words (BoW) and TF-IDF spam
detection algorithms using text data containing 747 spam message instances. They used a
variety of machine learning approaches to classify spam and were able to achieve an
accuracy of 97.99% and precision of 98.97%. For spam text identification, they found just a
minor difference in performance between the BoW and TF-IDF approaches.

One hot encoding

Every word or phrase in the given text data is stored as a vector with only the values 1
and 0. Every word is represented by a separate hot vector, with no two vectors being
identical. The sentence’s list of words can be defined as a matrix and implemented using
the NLTK python package because each word is represented as a vector.

Word embedding
One-hot encoding is ideal when we just have a little amount of data. Because the
complexity develops substantially, we can use this method to encode a vast vocabulary.
Comparable words have similar vector representations in word embedding, which is a

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 form of word representation technique. Because each word is mapped to a different vector
and the technique resembles a neural network, it is usually referred to as deep learning.

Word2Vec
To process text made up of words, this approach transforms words into vectors and
works in the same way as a two-layer network. Each word in the corpus is allocated a
matching vector in the space. Word2vec employs either a continuous skipgram or a
continuous bag of words architecture (CBOW). In the continuous skipgram, the current
word is utilized to predict the neighboring words, whereas in the CBOW model, a middle
word is predicted based on the surrounding or neighbouring words. The skip-gram
model can accurately represent even rare words or phrases with a small quantity of
training data, but the CBOW model is several times faster to train and has slightly better
accuracy for common keywords. The word2vec approach has the advantage of allowing
high-quality word embedding to be learned in less time and space. It makes it possible to
learn larger embeddings (with greater dimensions) from a much larger corpus of text.

Glove word embedding

It’s an unsupervised model for generating a vector for word/text representation. The
distance between the terms is determined by their semantic similarity. Pennington,
Socher & Manning (2014) were the first to use it to their studies. It employs a co-
occurrence matrix, which shows how frequently words appear in a corpus, and is based on
matrix factorization techniques. The Eq. (3) shows the calculation for the co-occurrence
probability of the texts in each word embedding
Pac
F ðta ; tb ; tc Þ ¼ (3)
Pbc
where,
The co-occurrence probability for the texts ta and tc is Pac
The co-occurrence probability for the texts tb and tc is Pbc
The normal texts/words that appear in a document are ta and tb and the probe text is tc
When the aforementioned ratio is ‘1’, the probe text is related to ta rather than tb
Table 11 summarizes some of the existing research studies that use various feature
extraction approaches such as TF-IDF, Bag of Words (BOW), N-grams, and Word
embedding techniques such as Glove and Word2Vec.

SPAM TEXT CLASSIFICATION TECHNIQUES

Text classifiers can organize and categorize practically any sort of material, including
documents and internet text. Text classification is an important stage in natural language
processing, with applications ranging from sentiment analysis to subject labelling and
spam detection. Text classification can be done manually or automatically, however in
the manual approach, a human annotator assesses the text’s content and categorizes it
correctly. Machine learning techniques and other Artificial Intelligence (AI) technologies
are used to automatically classify text in a faster and more accurate manner utilizing

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 Table 11 Existing works that employ various text feature extraction techniques.
S.No Author Dataset Classification approach Merits Limitations Result
1 Inuwa-Dutse, Honeypot, SPD Support Vector Machine Real time spam Need to deal with the Accuracy-97.71%
Liptrott & manually and (SVM), Random Forest detection is possible presence of lengthy Precision-99%
Korkontzelos automatically (RF), Multi-Layer and the proposed tweets on spamming Recall-97%
(2018) annotated spam Perception (MLP), Gradient feature set increases activity. F-Score-98%
dataset Boosting and Max.Entropy the system accuracy
2 Aiyar & Shetty 13,000 comments RF, SVM, Naive Bayes (NB) Machine Learning The use of better word F1-Score-0.97
(2018) from YouTube with N-grams based features (ML) models with N- representation like
channels grams has helped to Word2Vec is needed
improve to improve system
the classification performance
accuracy
3 Chu, Widjaja & 774 spam RF, Decision Trees (DT), Content and Behaviour Need to explore more Accuracy-94.5%
Wang (2012) campaigns in 1, Decision Table, Random features were features to build a FPR-4.1%
31,000 Tweets Tree, KStar, Bayes Net and combined to build an robust model for FNR-6.6%
Simple Logistic automatic spam spam classification
detection model.
4 Alharthi, More than 10,000 Long Short Term Memory Time requirement to System classification Accuracy-0.97
Alhothali & Arabic tweets (LSTM) with word classify the tweets is accuracy depends on Precision-0.98
Moria (2021) collected with embedding feature very less compared to tweet length Recall-0.95
Twitter API representation the state-of-the art F1-score-0.97
methods
5 Liu, Pang & 97,839 Restaurant Machine Learning (ML) Could capture There is a need to Recall-0.80
Wang (2019) (RES) and 31,317 techniques and Bi-LSTM sophisticated analyze the use of Precision-0.82
Hotel review spammer activities other effective F1-score-0.81
dataset (HOS) using multimodal features to improve
neural network the performance
model
6 Fusilier et al. Hotel review corpus SVM, K-Nearest Neighbor Lexical content and Need to build a hybrid F1-score-0.87
(2015) consisting of 1, and Naïve Bayes (NB) stylistic information feature set combining
600 reviews were captured better character and word
using character n- n-grams
grams
7 Wu et al. (2017) 10 day real-life RF, Multi-Layer Perceptron Variations in The model needs to be Accuracy-99.35
Twitter dataset of (MLP) and Naïve Bayes spamming activities adaptable to new Recall-91.03%
1,376,206 spam are captured within a characteristics Precision-95.84%
and 6,73,836 short span of time. F-measure-
non-spam tweets 93.37%

automatic text classification models. As shown in the Fig. 4 below, there are three
techniques of classifying the text.

Spam classification using rule based systems

They work by sorting the text into distinct groups using handcrafted linguistic rules. The
entering text is classified using semantic factors based on its content. Certain terms can
help you evaluate whether or not a text message is spam. The spam text has a few
distinctive phrases that help differentiate it from non-spam language. The document is
classified as spam when the number of spam words in it exceeds the number of non-spam
(ham) terms. They operate by employing a set of framed rules, each of which is given a

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 Figure 4 Various text-preprocessing techniques. Full-size  DOI: 10.7717/peerj-cs.830/fig-4

Table 12 Existing research works on spam classification using rule-based systems.

S.No Author Dataset Classification Merits Limitations Result
approach
1 Shrivastava Corpus of 2,248 emails Rule based spam Combination of Genetic Need to increase the size of Accuracy-82.7%
& Bindu with 1,346 spam and detection filter Algorithm with e-mail dataset and in-depth analysis Precision-
(2014) ham texts with some filtering methods facilitates of parameters of Genetic 83.5%
assigned weights efficient spam detection algorithm is required
2 Vanetti 1,260 Facebook Flexible rule-based Automatic filtering of Care should be taken to handle Precision-81%
et al. messages from system is used to unwanted messages from the extraction of contextual Recall-93%
(2013) Italian groups customize the Online Social Networks is features for better F1-Score-87%
filtering criteria. made possible. discrimination of samples.
3 Saidani, Adi Enron Corpus Manually and Domain categorization used Continuous enhancement and Accuracy-0.98
& Allili consisting of 2,893 Automatically in this work has helped to updation of semantic features Precision-0.98
(2020) messages with 2,412 extracted rules improve the filter is needed. Recall-0.98
ham and 481 ham from labelled performance F1-measure-
text. emails 0.97
4 Luo et al. SpamAssassin corpus Rule extraction, Dynamic adjustment of static Value of threshold has an Accuracy-98.5%
(2011) with 4,150 spam and optimization and rules for improving the impact on classification False Positive
1,897 ham emails rule filtering spam filter is made possible. performance and it has to be Rate-0.42%
models are used taken care of. False Negative
Rate-4.7%
5 Fuad, Deb Email corpus with 271 Fuzzy Inference The system is made adaptive Need to train the system with a Accuracy-90%
& Hossain training and 30 test System with a set by making use of effective large corpus to improve the Precision-83%
(2004) email text of Fuzzy rules fuzzy rules. accuracy. Recall-72%

weight. The spam text corpus is scanned for spam content, and if any rules are found in the
text, their weight is added to the overall score. Table 12 summarizes some of the existing
works on spam classification using rule-based systems.
Based on the previous works on spam classification using rule-based techniques given
in Table 12, we can conclude that rule-based techniques are well-appreciated by
researchers for their importance in spam text classification. SpamAssassin is open source
software that aids in the creation of rules for various categories and is preferred by
spam detection researchers. Some rule-based systems rely on static rules that can’t be
changed, so they can’t deal with constantly changing spam content. To improve the
method’s ability to detect spam, the established rules must be updated on a regular basis.
To deal with the varying nature of spam, the automatic rule generation concept can be
used. For complex systems, rule-based systems have significant drawbacks in terms of time
consumption, analysis complexity, and rule structuring. They also require more contextual
features for effective spam detection, as well as a large training corpus.

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 Machine Learning (ML) techniques for spam classification
To detect spam reviews, a variety of machine learning techniques have been deployed.
There are two types of machine learning: supervised learning and unsupervised learning,
both of which are extensively utilized in NLP applications. Jancy Sickory Daisy &
Rijuvana Begum (2021) used the Nave Bayes method and the Markov Random Field to
circumvent the limitations of other filtering algorithms. By combining two algorithms, this
hybrid system was able to detect spam effectively while saving time and improving
accuracy. Dedeturk & Akay (2020) compared the performance of their proposed spam
filtering strategy, which is based on a logistic regression model, to that of existing models
such as Support Vector Machine (SVM) and Naive Bayes (NB). They tested their
algorithm on three publicly available e-mail spam datasets and discovered that it
outperformed the others in spam filtering. Nayak, Amirali Jiwani & Rajitha (2021)
employed a hybrid strategy that combined Nave Bayes and Decision Tree algorithms to
identify spam e-mails (DT). They were able to obtain an accuracy of 88.12% using their
hybrid approach. Table 12 covers a number of existing spam classification works that
employ various Machine Learning (ML) methodologies. To protect social media accounts
from spam, Sharma et al. (2021) used Decision Tree (DT) and K-Nearest Neighbor
(K-NN) classifiers. They tested their method using the UCI machine learning e-mail spam
dataset. With a classification accuracy of 90% and an F1-score of 91.5%, the Decision Tree
classifier produced better results. In their research, Raza, Jayasinghe & Muslam (2021)
found that multi-algorithm systems outperform single-algorithm systems when it comes
to spam classification. For e-mail spam detection, they compared the performance of
supervised and unsupervised machine learning algorithms. For better spam detection, the
supervised approach outperformed the unsupervised approach. Junnarkar et al. (2021)
used a two-step methodology to ensure that the mail people received was not spam.
They utilized URL analysis and filtering to see if any of the links in the email were
malicious or not. A total of five machine learning algorithms were investigated. On the
e-mail spam dataset, Naive Bayes and Support Vector Machine achieved the highest
accuracy of over 90%. The importance of machine learning techniques for spam text
classification is studied by Al-Zoubi et al. (2018), Singh et al. (2021), Tang, Qian & You
(2020) in their work in which they conclude that Machine Learning techniques overcome
the drawbacks of rule-based techniques for spam content detection.
Based on the prior work on spam classification with Machine Learning approaches
presented in Table 13, we can conclude that Machine Learning techniques are highly
valued by researchers for their importance in spam text classification. Machine learning
has the ability to adapt to changing conditions, and it can help overcome the limitations of
rule-based spam filtering techniques. Support Vector Machines (SVM), a supervised
learning model that analyses data and identifies patterns for classification, is among the
most significant machine learning techniques. SVMs are straightforward to train,
and some researchers assert that they outperform many popular social media spam
classification methods. However, due to the computational complexities of the data
input, the resilience and usefulness of SVM for high dimension data shrinks over time.

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 Table 13 Existing research works on spam classification using machine learning.
S.No Author Dataset Classification Merits Limitations Result
approach
1 Kontsewaya, 4,360 non-spam and Logistic Regression Presented a comparative Better DL based feature Accuracy-0.99
Antonov & 1,368 spam samples (LR), Naïve Bayes analysis of different learning strategies can Precision-0.97
Artamonov from the Kaggle (NB), K-Nearest ML algorithms be employed for Recall-0.99
(2021) Dataset Neighbor (K-NN) extracting relevant F-measure-0.98
and Decision Trees features.
(DT)
2 Mohammed Email-1,431 dataset SVM, K-NN, NB and Instead of using spam Less number of training Accuracy-85.96%
et al. (2013) DT trigger words, which samples used (272 ham Precision-84.5%
may fail, a lexicon- and 1,219 spam). Need F1-score-85.12
based approach is used for a better feature
to filter the data. extraction technique
3 Watcharenwong 1,200 Labelled posts Random Forest (RF) Social features like Need to use image Precision-98.19%
& Saikaew crawled from comments etc., are features to get improved Recall-98.12%
(2017) Facebook using a combined with textual results F1-score-98.15%
webcrawler features yields better
results
4 Dhawan & 25,847 Twitter users DT, NN, SVM, NB Graph and Content Need to analyze the use of Precision-1
Simran (2018) with 500K tweets are based features Deep Learning (DL) Recall-0.41
collected using extracted from Twitter techniques and bring in F-measure-0.58
Twitter API and a aids in improving more metrics for
Web crawler model’s performance performance evaluation.
5 Ban et al. (2018) Textual data collected SVM & NN Hybrid architecture of Only a few performance Precision-85%
from Twitter and SVM with NN helped metrics is evaluated to Recall-84%
Facebook with spam to improve the determine the model’s
and on-spam content classification results efficiency
6 Dewan & 4.4 million Facebook RF Automatic identification The labelled spam dataset Accuracy-86.9%
Kumaraguru posts acquired using of spam text is done was gathered through Precision-95.2%
(2015) Graph API with 42 features using crowdsourcing and may
ML techniques be biased.
7 Kumar et al. Restaurant reviews LR, K-NN, NB, RF, For effective spam It is necessary to adjust Accuracy-0.76
(2018) from Yelp.com SVM identification, uses the model to new F1-Score-0.79
both univariate and characteristics and
multivariate improve its efficiency.
distribution across
user ratings.
8 Saeed, Rady & Opinion spam corpus Rule-based and The model’s Spam detection efficiency Accuracy-95.25%
Gharib (2019) (DOSC & HARD) Machine learning performance was could be improved Recall-91.75%
datasets with 1,600 classifiers (NB, increased by using N- using Deep Learning Precision-
opinion reviews in SVM, K-NN, RF and gram feature (DL) techniques 98.66%
English NN) extraction and F1-Score-95.08%
Negation handling.
9 Mani et al. Opinion spam corpus NB, RF and SVM The ensemble strategy It is necessary to develop a Accuracy-87.68%
(2018) dataset with 1,600 aided in obtaining a control mechanism to Precision-0.89
reviews higher accuracy score. reduce the propagation Recall-0.85
of fraudulent reviews.
10 McCord & Random collection of RF, NB and K-NN User and Content based Need a larger Twitter Precision-95.97
Chuah (2011) tweets from 1,000 features with RF dataset for evaluating Recall-0.95
Twitter accounts classifier was the effectiveness of the F-measure-0.95
containing both spam successful in model
and non-spam text identifying spam and
non-spam tweets

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 Another machine learning algorithm that has been successfully used to detect spam in
social media text is the decision tree. When it comes to training datasets, decision trees
(DT) require very little effort from users. They suffer from certain disadvantages, such as
the complexity of controlling tree growth without proper pruning and their sensitivity to
over fitting of training data. As a consequence, they are rather poor classifiers and their
classification accuracy is restricted. A Naive Bayes (NB) classifier simply applies Bayes’
theorem to the perspective classification of each textual data, assuming that the words in
the text are unrelated to one another. Because of its simplicity and ease of use, it is
ideal for spam classification and it could be used to detect spam messages in a variety of
datasets with various features and attributes. An ensemble strategy, which combines
various machine learning classifiers, can also be utilized to improve spam categorization
jobs. We can deduce from various studies on Machine Learning for spam classification
that ML techniques occasionally suffer from computational complexity and domain
dependence. The researchers recommend Deep Learning (DL) techniques to avoid such
limitations in ML techniques for spam classification because some algorithms take much
longer to train and use large resources based on dataset.

Hybrid approach for spam classification

To increase spam classification performance, hybrid spam detection systems combine a
machine learning-based classifier with a rule-based approach. To detect spam in emails,
Abiramasundari (2021) utilized a hybrid technique that comprised “Rule Based Subject
Analysis” (RBSA) and machine learning algorithms. Their rule-based solution involves
assigning suitable weights to spam material and generating a matrix that is then submitted
to a classifier. They tested their method on the Enron dataset (email corpus), and their
proposed work with the SVM classifier achieved a very low positive rate of 0.03 with a 99%
accuracy. Venkatraman, Surendiran & Arun Raj Kumar (2020) employed a semantic
similarity technique combined with the Naive Bayes (NB) machine learning algorithm to
classify spam material. The proposed “Conceptual Similarity Approach” computes the
relationship between concepts based on their co-occurrence in the corpus. They tested
their hybrid spam classification strategy using the Spambase and Enron corpus datasets.
They have a near-perfect 98% accuracy rate. Wu (2009) used a novel technique to spam
detection in their work, merging Neural Networks (NN) with rule-based algorithms.
They classified spam content using Neural Networks, rule-based pre-processing, and
behavior identification modules with an encoding approach. They tested their approach
on an email corpus containing lakhs of emails and scored a 99.60% spam detection
accuracy score.

DEEP LEARNING (DL) APPROACHES FOR SPAM

CLASSIFICATION
Deep learning models are gaining popularity among NLP researchers due to their ability to
solve challenging problems (Kłosowski, 2018; Torfi et al., 2020). Deep learning is based
on the idea of building a very large neural network inspired by brain activities and training
it using a massive amount of data. They can cope with the scalability issue and extract

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 the features from the data automatically. The most popular deep learning models among
NLP researchers are Convolutional Neural Networks (CNN) and Long Short Tern
Memory (LSTM) networks. Convolutional Neural Networks (CNN), one of the most
important and extensively used Deep Learning approaches, has received a lot of attention
in recent times for performing NLP tasks. It has been used successfully for sentiment
analysis (Kim & Jeong, 2019), image (Sharma, Jain & Mishra, 2018) and text categorization
(Song, Geng & Li, 2019), pattern recognition (Mo et al., 2019), and other tasks. For text
categorization, Lai et al. (2015) used a recurrent structure to capture contextual
information from textual data. Their technique was able to capture semantic information
from text and outperformed CNN in classifying text texts. Tai, Socher & Manning (2015)
employed the Long Short Term Memory Network (LSTM) to capture sequential
information in textual data, and they built a tree LSTM model that could perform well for
NLP applications. Basyar, Adiwijaya & Murdiansyah (2020) built a Long Short Term
Memory (LSTM) network and a Gated Recurrent Unit (GRU) model to detect spam in
the Enron e-mail spam dataset, which contained 34,519 records. The LSTM model
outperformed the GRU model in spam detection, achieving an accuracy of 98.39%.
Alauthman (2020) employed the Gated Recurrent Unit-Recurrent Neural Network
(GRU-RNN) to recognize Botnet spam E-mails. On the SPAMBASE dataset, which
included 4,601 spam and 2,788 non-spam e-mails, they achieved an accuracy of 98.7%.
They evaluated the performance of GRU with several machine learning algorithms, but the
GRU-based strategy produced the best results for spam detection. Hossain, Uddin &
Halder (2021) used feature selection techniques including Heatmap, Recursive Feature
Elimination, and Chi-Square feature selection techniques, along with Deep Learning
models such as RNN, to select the most effective features for spam e-mail detection.
On spam text information obtained from the UCI machine learning repository, they
achieved a 99% accuracy. Tong et al. (2021) used a deep learning model based on LSTM
and BERT to overcome issues such as unfair representation, inadequate detection effect,
and poor practicality in Chinese spam detection. They created this model to capture
complex text features using a long-short attention mechanism. In their work to detect
spam reviews related to hotels, Liu et al. (2022) used a combination of Convolution
structure and Bi-LSTM to extract important and comprehensive semantics in a document.
They could be able to outperform current methods in terms of classification performance
by achieving an F1-Score of around 92.8. There are many other research works
(Crawford & Khoshgoftaar, 2021; Bathla & Kumar, 2021) employing Deep Learning (DL)
techniques for spam detection that could capture contextual information of text for spam
identification.
Based on the prior work on spam classification with Deep Learning approaches
presented in Table 14. These Deep Learning techniques definitely helps in improving
the performance of the spam detection model and also helps in reducing the effects of
over-fitting that is seen in Machine Learning models. Unlike ML techniques, deep learning
methods do not necessitate a manual feature extraction process or a large amount of
computational resources. It can adapt to a wide range of spam content found in social
media text and will be very effective at extracting spam data from the text. Based on

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 Table 14 Existing research works on spam classification using deep learning.
S.No Author Dataset Classification Merits Limitations Result
approach
1 Alom, 1. Twitter social Convolutional Neural Combination of tweet Using only textual data i.e Accuracy-99.32%
Carminati & honeypot dataset Network (CNN) text with meta data tweets the system could not Precision-
Ferrari (2020) 2. Twitter 1KS- has helped to attain perform well 99.47%
10KN dataset good performance for Recall-99.9%
spam classification F1-Score-
99.68%
2 Feng et al. (2018) Sina Weibo dataset Convolutional Neural Detects the spam Complexity of the model Accuracy-91.36%
with 12,500 Network (CNN) content by utilizing false Positive
malicious URLs and with Word2Vec low computing Rate-8.82% and
12,500 normal resources False Negative
URLs Rate-8.54%
3 AbdulNabi & Open source Fine-tuned BERT Spam detection Need to utilize a large input Accuracy-0.98
Yaseen (2021) SpamBase dataset (Bidirectional efficiency is improved sequence for better training F1-Score-0.98
with 5,569 emails Encoder with the help of BERT of model.
and Kaggle spam Representations word embedding
filter dataset from Transformers) approach
with Word2Vec
approach
4 Seth & Biswas Image-Dataset with CNN with Multimodal (Image Need to improve the neural Accuracy-98.11%
(2017) 1,521 spam images multimodal data +Text) technique network model for F1-Score-0.98
and 1,500 ham (Image and Text) helped to achieve achieving better accuracy
images. greater accuracy by tuning the hyper
Text-Enron spam compared to parameters
dataset unimodal inputs
5 Xu, Zhou & Liu MicroblogPCU Self-attention Semantic and Computational time and Accuracy-0.91
(2021) dataset-2,000 spam BiLSTM with Contextual data from resources required by the Recall-0.89
and non-spam data ALBERT model- Tweets are captured model has to be reduced. F1-score-0.90
Weibo dataset- word vector model using the Bi-LSTM
95,385 weibo tweets of BERT model with self-
attention mechanism
6 Ma et al. (In Twitter and Recurrent Neural RNN model with Massive unlabeled data from Accuracy-0.88
press) SinaWeibo datasets Networks (RNN) multiple hidden and social media reduces the Precision-0.85
with 2,313 and with extra hidden embedding layers help system performance. Recall-0.95
2,351 rumors layers to reduce the spam Works well for Weibo F1-Score-0.89
detection time. dataset compared to
Twitter
7 Neisari, Rueda Single domain hotel Un-supervised Self Semantic information is Need to improve the Accuracy-0.87
& Saad (2021) review dataset with Organized Maps captured well with the performance of SOM F1-measure-
800 reviews (SOM) with CNN help of SOM to model by including 0.88
(Dataset1) enhance the spam additional layers and
Multi-domain detection performance features.
dataset with 2,840
reviews (Dataset2)
8 Shahariar et al. Single domain hotel CNN and Bi-LSTM Word2Vec approach Data labelling process need Accuracy-94.56%
(2019) review dataset with with Word2Vec has helped to get to be improved and F1-measure-
800 reviews and method better feature vector requires more training 95.2%
Yelp spam review representations to get samples (1,600 reviews) to
dataset with 2,000 efficient results. improve the classification
reviews performance.
(Continued )

Kaddoura et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.830 19/28

 Table 14 (continued )
S.No Author Dataset Classification Merits Limitations Result
approach
9 Makkar & WEBSPAM-2007 LSTM model It provides cognitive Need to tune the algorithm to Accuracy-96.96%
Kumar (2020) dataset containing ability to search handle large scale data from F1-measure-
222 spam and 3,776 engine for automatic web 94.89%
non-spam web webspam detection.
pages.
10 Zhuang et al. WEBSPAM-UK2006 Deep Belief Networks Algorithm’s Proposed algorithm’s Accuracy-0.94
(2021) and WEBSPAM- (DBN)-Stacked performance is performance is dependent Precision-0.95
UK2007 datasets Restricted improved by on selection of appropriate Recall-0.95
with spam and non- Boltzmann Machine employing a reference examples.
spam labels (RBM) preference function
which is based on
DBN

previous research, we can deduce that combining word-embedding techniques with Deep
Learning methods improves spam classification performance. However, with less
training data, it is more difficult to avoid over-fitting, and the presence of unlabeled text in
the input corpus will lower performance. The deep learning method is used to classify text
that saves a lot of manpower and resources while also improving text classification
accuracy.

CHALLENGES IN SPAM DETECTION/CLASSIFICATION

FROM SOCIAL MEDIA CONTENT
Spam content on social media continues to rise as people’s use of social media grows
dramatically. The technology underlying spam spread is amazing, and some social
media sites were unable to correctly identify spam contents/spammers. Some legitimate
social media users manufacture duplicates in order to communicate with a group of
recognized pals. It is tough to distinguish between a spammer and a legitimate user with a
duplicate profile. Spammers also employ many fake identities to distribute dangerous and
fraudulent material, making it harder to track them down. A spammer may also
employ social bots to automatically post messages based on the user’s interests. Many
businesses use “crowdsourcing” to enhance production, in which some people are paid to
offer false reviews about a product that is not good. The machine learning method for
spam detection suffers from over-fitting and sometimes suffers from a lack of training
samples. They may also encounter difficulties if the spammer is intelligent and quick
enough to adapt. When the input dataset is quite large, ML approaches suffer from
temporal complexity, and memory requirements are also an issue. If there are undesirable
features in the dataset, the classifier’s performance suffers, and an efficient feature selection
algorithm is required.
Unsupervised learning suffers from a storage shortage, as well as a scarcity of efficient
spam detection methods. As a result, there is a strong need to pursue a method that is
flexible and efficient, such as Deep Learning, in order to tackle the challenges encountered
by traditional Machine Learning methodologies. Spammers also employ Deep Learning

Kaddoura et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.830 20/28

 algorithms to manipulate social media material in order to generate spam. These bogus
contents developed using Deep Learning algorithms are difficult to detect, necessitating
more effort to resist them. If there is a shortage of properly annotated data available, the
notion of transfer-learning might be used as an alternative to Machine Learning.

OPEN ISSUES AND FUTURE DIRECTIONS

Some of the issues in spam detection are the presence of sarcastic text, multilingual data,
and improper labelling of the datasets. Many researchers use APIs to gather data related to
a given language and geographical area, there is a bias in the data collected through
social media. Some studies employ raw data without much pre-processing, which results in
duplicated features and lower classification performance. Some datasets exhibit a class
imbalance, for example, the ‘spam’ class has a large number of samples whereas the ‘ham’
class has a small number of samples.
There are a limited number of labelled datasets available for spam text, as well as a
limited number of attributes available in these text datasets, which is a problem. For
efficient research, a dataset with correct labelling is required, as is large computational
power in the case of a large dataset. Only a few studies have used deep learning techniques
and semantic approaches to detect spam. Exploring the use of multimodal content
(text and images) from social media for social media would be a significant future
challenge.

CONCLUSION
We have described numerous strategies for spam text identification in depth in our
systematic literature review on spam content detection and categorization. Our research
also looked into the various techniques for pre-processing, feature extraction, and spam
text classification. This survey will assist researchers in conducting research in the field of
social media spam detection as it highlights some of the best works done in this field.
We’ve also provided details on a number of databases that can be used for spam detection
studies. The various previous works on spam text pre-processing, feature extraction,
and classification will aid researchers in determining the most appropriate strategies for
their research in this area. In future development, we’d like to include some other spam
detection approaches, as well as their benefits and drawbacks.

ADDITIONAL INFORMATION AND DECLARATIONS

Funding
This work was funded by Zayed University–Start-up research grant (Grant number
R20081). The funders had no role in study design, data collection and analysis, decision to
publish, or preparation of the manuscript.

Grant Disclosures
The following grant information was disclosed by the authors:
Zayed University: R20081.

Kaddoura et al. (2022), PeerJ Comput. Sci., DOI 10.7717/peerj-cs.830 21/28

 Competing Interests
Jude Hemanth Duraisamy is an Academic Editor for PeerJ.

Author Contributions
 Sanaa Kaddoura conceived and designed the experiments, performed the experiments,
analyzed the data, performed the computation work, prepared figures and/or tables,
authored or reviewed drafts of the paper, and approved the final draft.
 Ganesh Chandrasekaran conceived and designed the experiments, prepared figures and/
or tables, and approved the final draft.
 Daniela Elena Popescu analyzed the data, authored or reviewed drafts of the paper, and
approved the final draft.
 Jude Hemanth Duraisamy performed the experiments, authored or reviewed drafts of
the paper, and approved the final draft.

Data Availability
The following information was supplied regarding data availability:
This is a literature review.

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