Page No: 434-440
Abstract : The rise of social media has created a new platform for communication and
interaction, but it has also facilitated the spread of harmful behaviors such as cyberbullying.
Detecting and mitigating cyberbullying on social media platforms is a critical challenge that
requires advanced technological solutions. This paper presents a novel approach to
cyberbullying detection using a combination of supervised machine learning (ML) and natural
language processing (NLP) techniques, enhanced by optimization algorithms. The proposed
system is designed to identify and classify cyberbullying behavior in real-time, analyzing textual
data from social media posts to detect harmful content. The model is trained on a large dataset
of labeled instances of bullying and non-bullying content, using supervised ML algorithms such
as Support Vector Machines (SVM), Decision Trees, and Random Forest. NLP techniques,
including sentiment analysis, keyword extraction, and text vectorization, are employed to
preprocess and transform the data into a format suitable for machine learning. To optimize the
performance of the detection model, techniques such as Grid Search, Genetic Algorithms, and
Particle Swarm Optimization are used to fine-tune hyperparameters, resulting in improved
accuracy and reduced false positives. The system's effectiveness is validated through
experiments conducted on various social media platforms, demonstrating its potential to detect
cyberbullying with high precision. Future work will focus on enhancing the model's adaptability
to emerging slang and evolving language patterns in social media.
Key words: Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural
Language Processing (NLP), Optimization Techniques
Keyword Cyberbullying Detection, Social Media, Supervised Machine Learning, Natural
Language Processing (NLP), Optimization Techniques