Predicting Cyberbullying on Social Media in the Big Data Era Using Machine Learning Algorithms

Description

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 is highlighted in this work. 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, issues
and challenges have been highlighted as well, which present new research directions for researchers to
explore.