Detecting Twitter Fake Accounts using Machine Learning and Data

Reduction Techniques

Ahmad Homsi, Joyce Al Nemri, Nisma Naimat, Hamzeh Abdul Kareem, Mustafa Al-Fayoumi

and Mohammad Abu Snober

Department of Computer Science,

Princess Sumaya University for Technology

, Khalil Al-Saket Street, Amman, Jordan

Keywords: Twitter, ML, Detecting Fake Accounts, Spearman's Correlation, PCA, J48, Random Forest, KNN, Naive

Bayes.

Abstract: Internet Communities are affluent in Fake Accounts. Fake accounts are used to spread spam, give false

reviews for products, publish fake news, and even interfere in political campaigns. In business, fake accounts

could do massive damage like waste money, damage reputation, legal problems, and many other things. The

number of fake accounts is increasing dramatically by the enormous growth of the online social network; thus,

such accounts must be detected. In recent years, researchers have been trying to develop and enhance machine

learning (ML) algorithms to detect fake accounts efficiently and effectively. This paper applies four Machine

Learning algorithms (J48, Random Forest, Naive Bayes, and KNN) and two reduction techniques (PCA, and

Correlation) on a MIB Twitter Dataset. Our results provide a detailed comparison among those algorithms.

We prove that combining Correlation along with the Random Forest algorithm gave better results of about

98.6%.

1 INTRODUCTION

Facebook, LinkedIn, Twitter, Instagram, and other

social media networks, have been growing sharply

over the last century due to technology's growth.

Smartphones, tablets, laptops, and modern devices

helped people in utilizing wireless communication in

in an efficient and easy way. Nowadays, about 4.66

billion people worldwide use the Internet, and 4.14

billion are active users on social media (Johnson,

2021), which represents more than half of the world.

Facebook is considered the biggest online social

network. Facebook has 2.2 billion monthly active

users as of the first month of 2021 (Mohsin, 2021),

whereas Twitter has 330 million monthly active users

and 145 million daily active users (Lin, 2020). Some

of those accounts are fake and lead to misbehaviour,

including political, fake-news-spreading, black-

mailing, misleading ads, terrorist propaganda, spam,

and hate speech. As a result, this will damage the

reputation reliability of the famous Online Social

Networks (OSNs) as fake accounts made it easy to do

such activities.

Online social Networks (OSNs) allow users

having some common ideas to communicate easily.

They are provided the access to many services, such

as: posting comments on their profiles and on the

others’ profiles, messaging and voice/video voice

chatting, buying and selling stuff and services,

sharing news and thoughts and planning/arranging

events.

Moreover, some government entities use OSNs to

provide their driven services, share their official news

and announcements to their citizens, and to share

information about different activities (Awasthi,

Shanmugam, Soumya and Atul, 2020).

A fake profile account is classified into a fake or

duplicated account. We call an account as a

“duplicate” when a user impersonates an account for

another person.

Hiding the real identity for a malicious account for

the reason of malicious activities has grown

dramatically over the last couple of years. The threats

of fake accounts are clustered against one’s reputation

and cause unnecessary confusion by unpredictable

notifications (Awasthi, Shanmugam, Soumya and

Atul, 2020).

Fake profiles, or sometimes called Cyber-Bots,

which are made by cyber-criminals are almost cannot

be distinguished from real accounts, thus makes the

authentication process of user accounts more

complex Some fake accounts are made to

Homsi, A., Al Nemri, J., Naimat, N., Abdul Kareem, H., Al-Fayoumi, M. and Abu Snober, M.

Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques.

DOI: 10.5220/0010604300880095

In Proceedings of the 10th International Conference on Data Science, Technology and Applications (DATA 2021), pages 88-95

ISBN: 978-989-758-521-0

impersonate a real person’s profile, whereas some

others are made as general accounts to serve as fake

follower (Gurajala, White, Hudson, Voter and

Matthews, 2016).

Machine Learning (ML) algorithms are used to

automate and improve the process of detecting fake

accounts in order to make decisions faster. On the

other hand, bots are also being developed to bypass

those detection algorithms and mechanisms. So, it is

a non-ending battle.

This paper is structured as follows: Section 2

introduces the literature review. Section 3 presents

the methodology used in our work, and Section 4

illustrates the comparison result regarding the

algorithms used. Section 5 shows conclusions and

highlights for future work.

2 LITERATURE REVIEW

Many detection techniques used to classify social

accounts by analysing some existing features. Some

other detection techniques include ML algorithms for

better classifying of accounts.

Authors (Singh, Sharma, Thakral, and

Choudhury, 2018) followed a technique by using

some existing fake accounts to train a machine

learning algorithm. They took a sample size of 20,000

accounts that considered to be fake. In their study,

real accounts had more than 30 followers on average.

So, the first parameter was 30+ followers. The fake

accounts had some prevalent details that certain

individuals adopt to build a fake account, which are:

1. Wrong age for passing eligibility.

2. Incorrect gender definition.

3. Fake image of the profile taken mainly

from the Internet.

4. Image of a gendered character distinct

from the gender set.

5. False Locations for the accounts.

(Khaled, Tazi and Mokhtar, 2018) identified fake

accounts and bots on Twitter by suggesting a new

algorithm. Some machine learning classification

algorithms are used to detect real or fake target

accounts, by using Support Vector Machine (SVM),

Neural Network (NN) algorithms. They combine both

algorithms in a new hybrid one named SVM-NN for a

successful identification of fake accounts. Indeed,

they applied feature selection and dimension reduction

techniques in their work. The accuracy of detecting

fake accounts in their new hybrid algorithm was 98%.

In (Rao, Gutha and Raju, 2020), a cluster

classification approach had been followed which

focused on machine learning. Authors used vector

machines and neural networks to classify fake

accounts. They represent a machine learning pipeline

algorithm to identify fake accounts instead of using

prediction for each account. Their algorithm used to

classify a cluster of Fake accounts if the same person

attempted to generate them. The method started with

selecting profiles to be tested and then extracts the

necessary features and passes them to a trained

classifier along with the feedback. Classifies were

used to classify accounts into fake or real.

In (Isaac, Siodia and Moctezuma, 2016), the

authors suggested a web service that utilized user

accounts and timelines to create an initial feature set

of 71 cheap variables. They separated event-based

highlights into metadata-based and content-based.

Metadata applied to all details on adornment

endorsing or representing the primary substance.

These highlights usually arise from the normal

computation of standard factual. The authors consider

centrality estimators and scattering ratios of less

erratic dispersions in detail. These distributions

include tweet interval rates, tweets spanning over

multiple time ranges, origins of over-posting, and the

sum of intuitive components such as URLs, hashtags,

and tweet mentions. They used feature extraction and

five classification algorithms, which are: random

forest, SVM, Naive bays, Decision tree, and Nnet.

They proved that Random Forest gave the best

accuracy of 94%.

3 RESEARCH METHODOLOGY

This part introduces our proposed model in detail.

Figure 1 shows the model which consists of three

main phases: data preprocessing, data reduction and

data classification. We started our work by processing

the dataset, then we included some reduction

techniques in the second phase. In the reduction

phase, the data was filtered and reduced using some

reduction mechanisms to make it ready for the

classification phase; where the filtered data went

through classification algorithms and the final results

showed up.

Figure 1: Design Approach.

We worked on Weka software, which is a

platform for ML that supports multiple practices of

machine learning in order to implement a proposed

Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques

approach or model (Bouckaert, Frank, Hall, Holmes,

Pfahringer, Reutemann, Witten, 2010). Below are the

main features of Weka:

Data Preprocessing: there are a large number of

methods used to filter data, starting from deleting

attributes and ending with more advanced

operations like PCA.

Classification: Weka consists of more than 99

classifiers split into Bayesian, lazy, function-based,

decision tables, tree, misc., and meta, each of which

has a couple of classification algorithms under it.

Clustering: Weka contains many clustering

schemes to support Unsupervised learning, such as

k-means and various hierarchical clustering

algorithms.

Attribute Selection: which consists of a group of

selection criteria and search methods to define the

set of important classes for the classification

performance.

Data Visualization: Data and values resulting from

operations can be represented in understandable

visual graphs.

3.1 Dataset Description

The dataset used in our research is the “MIB” dataset

(Cresci, Pietro, Petrocchi, Spognardi and Tesconi,

2015); which contains 5301 accounts divided as

follows:

3.1.1 Real Accounts

 The fake project Dataset contains 469 accounts,

100% human collected in a research project of

researchers at IIT-CNR, in Pisa-Italy.

 E13 (elezioni 2013) Dataset contains 1481

accounts all of them are real human accounts, as

they checked by two sociologists from the

University of Perugia, Italy.

3.1.2 Fake Accounts

 Fastfollowerz Dataset contains 1337 accounts

 Intertwitter Dataset contains 1169 accounts.

 Twittertechnology Dataset contains 845

accounts. Those accounts bought by researchers

from the market in 2013.

3.2 Dataset Preprocessing

MIB dataset has two feature of vectors types:

 Categorical features: such as name, screenname,

tweets.

 Numerical features: such as status-count, friends

count, profile_text_color.

We proceeded by the dataset by considering some

classification algorithms. We also consider the

numerical features types. Moreover, other categorcal

features had been converted into numerical features.

As the dataset contains many attributes, we tried

to test the most significant ones. Attributes that are

not significant were not included in our model. This

is important to apply different ML algorithms on the

dataset (Babatunde, Armstrong, Leng and Diepeveen

2014).

Table 1 lists all the features of the MIB dataset.

Table 1: All Dataset Vectors of the MIB dataset.

1 Profile-lin

-colo

18 Screen name

2 Profile-background-

colo

19 Protected

3 Profile-sidebar-fill-

colo

20 Verified

4 Profile-background-

tile

21 Description

rofile_banner_url 22 Update

6 Profile-text-colo

23 Dataset

7utc

offset 24 created

8 Default-profile-

image

25 url

9 Default-

rofile 26 Lan

10 Geo-enable

27 time

zone

11 Liste

-count 28 Location

12 Favourites-count 29

rofile_image_url

13 Friends-count 30 Name

14 Followers-count 31 ID

15 Statuses-count 32 profile_image_url

https

16 profile_background_

image_url_https

33 profile_backgrou

nd_

image_url

17 Profile-sidebar-

border-color

34 Profile-use-

background-

ima

After that, we had normalized the data as part of

the data preprocessing phase. The goal here is to

transform the distributed large numerical values to a

common scale of [0,1], without twisting the values

range-differences in order not to lose the information.

In addition, this step is also essential for some

algorithms to model the data appropriately.

When the data distribution is random and

ambiguous or when the distribution is not Gaussian,

then using normalization is a good technique, as

normalization refers to rescaling the selected

attributes from their original values to the scale of 0

DATA 2021 - 10th International Conference on Data Science, Technology and Applications

to 1 (Ramos-Pollán, Guevara-López, Suárez-Ortega,

and et al., 2012)

3.3 Data Reduction

Principal Component Analysis (PCA) and

Correlation are data reduction methodologies that

used according to their advantages. Some of

advantages of PCA are: removing correlated features,

improving algorithm performance by reducing the

number of features used, reducing overfitting and

improving visualization. While some of the

Correlation advantages are: showing the strength of

the relationship between two variables and gaining

the quantitative data which can be easily analysed.

Here, we describe both techniques in some details.

PCA is a dimensionality reduction method that is

used in the big datasets by converting the large dataset

variables into smaller ones without losing the

information of those values.

The reduction is made by

identifying directions, which is called principal

components, and increase the data variety to the max.

By reducing the number of components, samples

can be shown as small numbers instead of values for

large numbers of variables.

Samples then can be organized in a way which

makes it possible to visually weight differences and

similarities between samples and determine whether

samples can be grouped or not (Ringnér, 2008).

Spearman’s Correlation (aka rho), like all other

Correlation Coefficients, takes two variables (let’s

assume that they are called A and B) and calculates or

measures the strength of connotation between them.

All multi-variable Correlation studies show the

strength of connotation between two variables in a

single value will output a number between -1 and

+1. This output value is called the Correlation

coefficient.

When a positive value of the Correlation

Coefficient shows up, this means that these two

variables have a positive relationship between them

(when the value of variable A increases, the value of

variable B increases). However, when a negative

value of the Correlation Coefficient shows up, this

means that these two variables have a negative

relationship between them (when the value of

variable A increases, the value of variable B

decreases). While, when a value of zero in the

Correlation coefficient shows up, this means that

these two variables have no relationship between

them (Zar, 2014).

The resulted features after processing the dataset

are 15 features. Those features are illustrated in Table

Table 2: Selected Vectors of the Dataset.

1 Profile-lin

-colo

9 Default-

rofile

2 Profile-

ackgroun

-colo

10 Geo-enabled

3 Profile-sidebar-fill-

colo

11 Listed-count

4 Profile-

ack

roun

-tile

12 Favourites-count

5 Profile-sidebar-

orde

-colo

13 Friends-count

6 Profile-text-colo

14 Followers-count

7 Profile-use-

ackgroun

-image

15 Statuses-count

8 Default-profile-

ima

3.4 Data Classification

Classification refers to the process of expecting the

class of provided data points. Sometimes,

targets/labels or categories have the same meaning of

classes. When a mapping function is predicted from

input variables into separate output variables, this

task is called predictive classification modeling.

In learner’s classification, there are two main

types: lazy learners and eager learners.

Lazy Learners. Algorithms work under this

approach are usually save the training data and hold

until the test data show up. After that, we conduct the

classification based on the most of the related data in

the saved training dataset. The time taken by lazy

learners is less, but the predicting time is more than

the eager learner. k-nearest neighbor and Case-based

reasoning are examples of those algorithms.

Eager Learners. In this approach, algorithms usually

build a classification model based on the given

training data to get the classification of the same data.

Eager learners should be able to bind to one

suggestion. This suggestion should cover the whole

instance space. Because of the model construction,

this type takes a long time for training but less time

for prediction. Examples of algorithms use this type

of classification are: Decision Tree, Naive Bayes, and

Artificial Neural Networks.

3.4.1 Classification Algorithms

Nowadays, there are many classification algorithms,

although there is no way to decide which one is better

than the other. It depends on the nature of the dataset

used and the type of the application.

Decision Tree Algorithm is a flowchart

structured-like graph, which considered as a

Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques

Supervised learning technique. It can be used for both

classification and regression tasks.

Moreover, it is a statistical-based algorithm where

attributes are selected at the tree-of-nodes beginning

at the root and ending at the leaves. This classification

used to split the data into subsets at each node (leaf)

depending on the attributes’ values (Alsaleh, Alarif,

Al-Salman, AlFayez and Almuhaysin, 2014). Figure

2 shows how the Decision Tree works.

J48 was previously named C4.5. It is used to

create a Decision Tree and can be used as a classifier

for the datasets. It is considered also as a statistical

classifier (Xindong, Vipin, Quinlan, Ghosh, Yang,

Motoda , Mclachlan, Ng, Liu, Yu, Zhou, Steinbach,

Hand and Steinberg, 2007). Figure 3 illustrates the

mechanism of J48 classifies.

Figure 2: Decision Tree (Kotsiantis and Sotiris, 2007).

Figure 3: J48 Classifier (Bhargava, Sharma, Bhargava and

Mathuria, 2013).

Random Forest is a machine learning algorithm

that considered as a supervised learning technique. It

creates several Decision Trees on the subset of data.

As well as, it combines each tree prediction to give

the final output prediction for the whole tree based on

all votes technique (Jehad, Rehanullah, Nasir and

Imran, 2012) (Pretorius, Bierman and Steel, 2016).

Moreover, Random Forest is used in Regression and

Classification of ML. It is proved the effectiveness of

this algorithm on large datasets compared to other

classifiers like: Neural Networks, Discriminant

Analysis and Support Vector Machines (SVM)

(Jehad, Rehanullah, Nasir and Imran, 2012).

One of the most important benefits of Random

Forest is that it can work with missing data, which is

the replacement of missing values by the variable that

is common in a particular node. The Random Forest

can also handle big data quickly, provide a higher

accuracy and prevent overfitting problems. One the

other hand, Random Forest requires many computa-

tional resources and large memory for storage, due to

the fact that it creates a lot of trees to save information

piped generated from hundreds of individual trees.

Figure 4 shows how Random Forest works.

Figure 4: Random Forest Classifier (Mennitt, Sherrill and

Fristrup, 2014).

K-Nearest Neighbors (KNN) is one of the

supervised learning techniques. It is used in the

classification and regression tasks, but it is used

especially in the classification. KNN works in two

ways: firstly, the K-Nearest Neighbors pushes a new

data in the training process of the dataset in order to

predict the results. Its classifier is based on just one

point of data that is closest (nearest neighbor)

between all neighboring data points, then the

prediction shows the results as one output. Thus, this

training relies only on one neighbor (Kolahdouzan

and Shahabi, 2004).

In a second way of KNN, all the

closest

neighboring data-points can contribute in assigning a

value called K, which is based on the distance

between test data and a neighbor class such as class A

(Kolahdouzan and Shahabi, 2004). In other words, it

calculates how many neighbors are close and belongs

to class A and how many neighbors are close and

belong to class B, and so on. After selecting the class

with the most belongings, the value of K is computed

and assigned (Mustaqim, Umam and Muslim, 2020).

This method is called “lazy learning” because of its

generalization for the training process of the dataset

after receiving a query on the system. one of the

drawbacks of K-Nearest Neighbors is that it is

sensitive to inconsistent data (noisy) and missing

value data. Figure 5

illustrates how the KNN

classifier works.

DATA 2021 - 10th International Conference on Data Science, Technology and Applications

Figure 5: KNN classifier (Moosavian, Ahmadi,

Tabatabaeefar and Khazaee, 2012).

Naive Bayes algorithm is a supervised learning

algorithm which acts as a classifier that can predict

data based on the class observations of the input data.

Also, it gives a probability distribution over many

classes, rather than output the most class that the

observation belongs to (Aridas, Karlos, Kanas,

Fazakis and Kotsiantis, 2019).

In fact, there are some reasons why to choose the

Naive Bayes algorithm:

 Fast ML algorithm that does not need much

time in operation, and it is easy to apply to

predict a class from the dataset.

 If independent predictors data is correct, the

Naive Bayes performs better than other

algorithms and demand less training dataset.

 Naive Bayes is considered as the best

algorithm for categorical variables like text

classification, sentiment analysis (Suppala

and Rao, 2019).

One of the drawbacks for Naive Bayes algorithm

is that it supposes that all features are separated or not

connected. So, we cannot know the relationship

between features. This algorithm faces a challenge

‘zero-frequency trouble’, (i.e. if the categorical

variable has a category in the testing dataset, but not

observed in the training dataset, then the pattern

assigns a 0 probability and will not able to make a

prediction). To overcome this problem, a smoothing

technique shall be used. Figure 6 describes the Naïve

Bayes classifier technique.

Figure 6: Naive Bayes classifier (Raschka, 2014).

4 EXPERIMENTAL RESULTS

AND DISCUSSION

In our experiment, we used the default settings of the

Weka software to determine the training percentage

to be 66% of the whole dataset, and the testing

percentage to be 34%.

The experiment was done by using two reduction

techniques which are: PCA and Correlation, and four

classification algorithms which are: Random Forest,

K-Nearest-Neighbour (KNN), J48, and Naive Bayes.

The data goes into one of the reduction techniques

then all of the four classification algorithms are

applied. For example, the data goes into PCA with

Random Forest algorithm, PCA with KNN algorithm,

and so on.

We have focused in this paper on two

measurements, which are Precision and Accuracy.

Higher Accuracy and precision denotes higher

percentage in detecting fake accounts. These two

measurements can be calculated using the below

equations:

𝑃𝑟𝑒𝑐𝑖𝑠𝑖𝑜𝑛



𝑃𝑅









(1)

𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦



𝐴𝐶𝐶









(2)

where, TP: True Positive, FP: False Positive, FN:

False Negative, TN: True Negative.

Our results of combining reduction techniques

along with classification algorithms are shown in

Table 3 and Table 4. In Table 3 where the Correlation

is used with classification algorithms, we observed

that the highest accuracy and precision is achieved

with Random Forest, while we got the lowest

accuracy and precision with Naïve Bayes algorithm

The same observation is deduced from Table 4.

From the same table, we noticed that combining

Correlation with classifier algorithms will give higher

accuracy and precision compared to PCA except the

case Naïve Bayes.

From our results, the highest accuracy was

achieved by using the Correlation reduction

technique along with the Random Forest

classification algorithm with 98.6%. In the second

place, the accuracy of detecting fake accounts was

98% for using Correlation along with J48. Then, the

next higher accuracy is achieved by combining PCA

along with Random Forest with 95.4%. After that, the

PCA along with KNN with 93.56%. Then, the

accuracy of 93.5% goes for using the Correlation

along with KNN. Below that, combining PCA along

with J48 comes with 93.1. Then, the PCA along with

Detecting Twitter Fake Accounts using Machine Learning and Data Reduction Techniques

Naive Bayes with an accuracy of 83.9% and lastly

comes the Correlation with Naive Bayes with 82.1%.

Our results are summarized in Figure 7, which

compares the accuracy values of combining both of

the correlation techniques along with all classifier

algorithms used in our study.

Figure 7: Accuracy results for algorithms used.

From the same figure, we conclude that in order

to get the most accurate and precise result we have to

combine Correlation technique with Random Forest

algorithm. However, using the Correlation technique

along with Naive Bayes algorithm leads to the lowest

accuracy and precision. In addition, our results show

that the use of any of the correlation techniques along

with KNN algorithm has almost no effect regarding

the accuracy and precision.

Moreover, the Naive Bayes algorithm is the only

classifier that gives a lower accuracy when combined

with the any of the Correlation technique compared

with all other algorithms. The reason goes back where

the Naïve Bayes algorithm performance is decreased

when data features are highly correlated.

Table 3: Overall Results of our experiment using

Correlation.

Correlation

TP FP Precision Accuracy

J48 0.980 0.029 0.980 98.0022

Random Forest 0.987 0.014 0.987 98.6681

KNN 0.936 0.083 0.935 93.5627

Naive Bayes 0.821 0.262 0.827 82.131

Table 4: Overall Results of our experiment using PCA.

PCA

TP FP Precision Accuracy

J48 0.932 0.080 0.932 93.1743

Random Forest 0.954 0.047 0.955 95.4495

KNN 0.935 0.081 0.935 93.5072

Naive Bayes 0.839 0.215 0.838 83.9068

5 CONCLUSIONS AND FUTURE

WORK

In this study, we aimed at studying the effect of two

correlation techniques along with some ML

algorithms. Our main goal is to get a better

understanding of the effects of the correlation with

the classifier algorithms in detecting fake accounts on

social media. Our work is based on MIB dataset and

done using Weka software. The data preprocesing

and reduction phases of our model were designed to

make the dataset applicable for the classification

process. After that, the data went through the

classification phase to determine the best accuracy

along with different machine learning algorithms.

For the data reduction phase, the Principal

Component Analysis (PCA) and Correlation were

used with four classifier algorithms, which are: J48,

Random Forest, KNN, and Naive Bayes. Results

show that Random Forest algorithm along with

Correlation data reduction gives the best accuracy of

98.6%. While Naive Bayes algorithm along with

Correlation data reduction achieve the lowest

accuracy of 82.1%.

There are still many different experiments and

methodologies and algorithms need to be tested and

are left for the future. We plan to use different

reduction techniques and to test different

classification algorithms by doing deeper

investigation and analysis.

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