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Predicting Cyberbullying on Social Media in the Big Data
Era Using Machine Learning Algorithms: Review of
Literature and Open Challenges
ABSTRACT:
Prior to the innovation of information communication technologies (ICT), social
interactions evolved within small cultural boundaries such as geo spatial locations.
The recent developments of communication technologies have considerably
transcended the temporal and spatial limitations of traditional communications.
These social technologies have created a revolution in user-generated information,
online human networks, and rich human behavior-related data. However, the
misuse of social technologies such as social media (SM) platforms, has introduced
a new form of aggression and violence that occurs exclusively online. A new
means of demonstrating aggressive behavior in SM websites are highlighted in this
paper. The motivations for the construction of prediction models to fight
aggressive behavior in SM are also outlined. We comprehensively review
cyberbullying prediction models and identify the main issues related to the
construction of cyberbullying prediction models in SM. This paper provides
insights on the overall process for cyberbullying detection and most importantly
overviews the methodology. Though data collection and feature engineering
process has been elaborated, yet most of the emphasis is on feature selection
algorithms and then using various machine learning algorithms for prediction of
cyberbullying behaviors. Finally, the issues and challenges have been highlighted
as well, which present new research directions for researchers to explore.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
 System : Pentium Dual Core.
 Hard Disk : 120 GB.
 Monitor : 15’’ LED
 Input Devices : Keyboard, Mouse
 Ram : 1 GB.
SOFTWARE REQUIREMENTS:
 Operating system : Windows 7.
 Coding Language : ASP.NET,C#.NET
 Tool : Visual Studio 2008
 Database : SQL SERVER 2005
REFERENCE:
MOHAMMED ALI AL-GARADI1, MOHAMMAD RASHID HUSSAIN2,
NAWSHER KHAN2, GHULAM MURTAZA1,3, HENRY FRIDAY NWEKE 1,
IHSAN ALI 1, GHULAM MUJTABA1,3, HARUNA CHIROMA 4, HASAN ALI
KHATTAK 5, AND ABDULLAH GANI, “Predicting Cyberbullying on Social
Media in the Big Data Era Using Machine Learning Algorithms: Review of
Literature and Open Challenges”, IEEE Access ( Volume: 7 ), 2019.

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Predicting Cyberbullying on Social Media in the Big DataEra Using Machine Learning Algorithms:Review of Literature and Open Challenges

  • 1. Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges ABSTRACT: Prior to the innovation of information communication technologies (ICT), social interactions evolved within small cultural boundaries such as geo spatial locations. The recent developments of communication technologies have considerably transcended the temporal and spatial limitations of traditional communications. These social technologies have created a revolution in user-generated information, online human networks, and rich human behavior-related data. However, the misuse of social technologies such as social media (SM) platforms, has introduced a new form of aggression and violence that occurs exclusively online. A new means of demonstrating aggressive behavior in SM websites are highlighted in this paper. The motivations for the construction of prediction models to fight aggressive behavior in SM are also outlined. We comprehensively review cyberbullying prediction models and identify the main issues related to the construction of cyberbullying prediction models in SM. This paper provides insights on the overall process for cyberbullying detection and most importantly overviews the methodology. Though data collection and feature engineering process has been elaborated, yet most of the emphasis is on feature selection algorithms and then using various machine learning algorithms for prediction of cyberbullying behaviors. Finally, the issues and challenges have been highlighted as well, which present new research directions for researchers to explore.
  • 2. SYSTEM REQUIREMENTS: HARDWARE REQUIREMENTS:  System : Pentium Dual Core.  Hard Disk : 120 GB.  Monitor : 15’’ LED  Input Devices : Keyboard, Mouse  Ram : 1 GB. SOFTWARE REQUIREMENTS:  Operating system : Windows 7.  Coding Language : ASP.NET,C#.NET  Tool : Visual Studio 2008  Database : SQL SERVER 2005 REFERENCE: MOHAMMED ALI AL-GARADI1, MOHAMMAD RASHID HUSSAIN2, NAWSHER KHAN2, GHULAM MURTAZA1,3, HENRY FRIDAY NWEKE 1, IHSAN ALI 1, GHULAM MUJTABA1,3, HARUNA CHIROMA 4, HASAN ALI KHATTAK 5, AND ABDULLAH GANI, “Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms: Review of Literature and Open Challenges”, IEEE Access ( Volume: 7 ), 2019.