Feature extraction of social media bots for pattern recognition and validation
Other Title(s)
استخراج خصائص-مميزات البوتس الخاصة بموقع التواصل الاجتماعية للتعرف على أنماطها و التحقق من صحتها
Dissertant
Thesis advisor
Comitee Members
Khasawinah, Baha al-Din
Uqayli, Ibrahim
al-Qassas, Rad
University
Princess Sumaya University for Technology
Faculty
King Hussein Faculty for Computing Sciences
Department
Department of Computer Sciences
University Country
Jordan
Degree
Master
Degree Date
2015
English Abstract
Online social networks attract more and more users every day.
With this huge number of users many attacks such as phishing and automated social engineering attacks emerge.
The latest of these attacks is the use of social media bots for malicious purposes.
This thesis focuses on Facebook as one of the most used Online Social Networks (OSN’s) that offers many opportunities not just for users to communicate, but it also enables business owners, famous figures and politicians to create their pages and keep in touch with their crowds easily.
The misuse of OSN’s will be discussed to show how social media bots are used to draft the public opinion, spread fake reputation for products and services and gather user’s information.
In our work we suggested a Facebook bot detector using machine learning.
We used a Facebook application designed to gather user’s information by asking their permission.
After gathering the user’s information we asked some social media bot providers to provide us with bot accounts to use this application so we can gather their information as well.
The gathered information formed a dataset of bots and humans where we had a total of 759 human and bot accounts.
Features were extracted from the database using python scripts and using Weka different machine learning algorithms were tested.
These algorithms were able to learn the behavior of Facebook bot accounts and compare it to the normal user’s accounts.
The random forest test gave us the best detection ratio that reached 96.31 %.
Main Subjects
Electronic engineering
Information Technology and Computer Science
Topics
No. of Pages
57
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Related work.
Chapter Three : System frame work.
Chapter Four : Machine learning analysis and results.
Chapter Five : Conclusion and future work.
References.
American Psychological Association (APA)
al-Lawzi, Daniyah Abd Zahir. (2015). Feature extraction of social media bots for pattern recognition and validation. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-651176
Modern Language Association (MLA)
al-Lawzi, Daniyah Abd Zahir. Feature extraction of social media bots for pattern recognition and validation. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology. (2015).
https://search.emarefa.net/detail/BIM-651176
American Medical Association (AMA)
al-Lawzi, Daniyah Abd Zahir. (2015). Feature extraction of social media bots for pattern recognition and validation. (Master's theses Theses and Dissertations Master). Princess Sumaya University for Technology, Jordan
https://search.emarefa.net/detail/BIM-651176
Language
English
Data Type
Arab Theses
Record ID
BIM-651176
