TITLE:
Machine Learning for Identifying Harmful Online Behavior: A Cyberbullying Overview
AUTHORS:
Rikin Shrestha, Rushit Dave
KEYWORDS:
Cyberbullying Detection, Natural Language Processing (NLP), Text Classification, Online Safety, Multilingual Detection Systems
JOURNAL NAME:
Journal of Computer and Communications,
Vol.13 No.1,
January
22,
2025
ABSTRACT: In this modern era, platforms for digital/social media and video games are growing daily. People are becoming dependent on them from all ages and with many positive aspects, but there are drawbacks as well, one of which is cyberbullying. Cyberbullying is a form of bullying that uses technological platforms to bully others. It has effects on victims mentally, emotionally, and physically, which include low self-esteem, acting violently, despair, increased stress/anxiety, depression, self-harming/suicide, etc. Findings from this research study justify that it affects young people more, impacting their emotional development and overall safety. Real-time cyberbullying detection identifies and protects the target from further abuse and its effects. This study aids in determining the seriousness of the issue and the vulnerabilities that individuals can take advantage of to bully others. Additionally, it will help to understand how various features of cyberbullying detection function assist in developing a strong and trustworthy system and making a healthy online community. Natural Language Processing (NLP) models assess the textual content and analyze hashtags and comments. Similarly, image context is analyzed using Optical Character Recognition (OCR), which converts images into a machine-readable format for further examination. There are also Deep Neural Network models, such as Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BLSTM). CNN is utilized for text/picture classification, LSTM is used for long-term dependency learning, and BLSTM expands the network’s input by encoding data in both forward and backward directions. Classifiers like Support Vector Machine (SVM) and Naïve Bayes help detect cyberbullying. A working cyberbullying detection system can detect cyberbullying on multiple platforms. A deeper understanding of each machine learning algorithm allows one to build a model that improves upon their predecessors. With models being developed for different attributes providing results with high accuracy, the cyberbullying detection system contributes by leading us to a healthier online community.