Abstract :

Cyberbullying Has Emerged As A Critical Issue In Modern Digital Communication Platforms, Significantly Impacting Users’ Psychological And Emotional Well-being. This Paper Presents An Intelligent Cyberbullying Detection System That Leverages Machine Learning (ML) And Natural Language Processing (NLP) Techniques To Automatically Identify Harmful Content On Social Media. Unlike Traditional Manual Monitoring Systems, The Proposed Approach Automates Text Classification By Preprocessing Data, Extracting Features, And Applying Classification Algorithms Such As Naïve Bayes, Logistic Regression, And Support Vector Machine (SVM). The System Is Implemented Using Python, Flask Framework, And SQLite Database, Ensuring Real-time Detection And Efficient Data Handling. Experimental Results Demonstrate High Accuracy And Performance, Making The System Suitable For Scalable Deployment In Social Media Platforms To Ensure Safer Online Environments.

Published:

07-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1382-1385

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to Cite