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Leveraging Deep Learning for Cyber Bullying Detection on Social Media Platforms: A Holistic Approach to Mitigate Online Harassment

Thanuja S, Dr. Priya V, Vanthanadevi S, Yuvanidhi S

Abstract

Cyberbullying is a growing concern on social media platforms. This research presents an automated system for detecting cyberbullying in social media messages using a combination of supervised machine learning (ML) and natural language processing (NLP) techniques. The system utilizes a pre-trained model to classify messages as bullying or non-bullying based on textual features. It combines traditional ML models, like Logistic Regression, with advanced deep learning methods such as Long Short-Term Memory (LSTM) networks to analyze user-generated content. Preprocessing steps, including tokenization, stop word removal, and TF-IDF vectorization, transform raw text into structured data for model training. The model is trained on a labeled dataset containing both bullying and non- bullying messages to improve classification accuracy. A Streamlit-based web application allows users to input messages and receive real-time feedback on whether the message is classified as cyberbullying. This system aims to assist social media platforms in identifying and preventing cyberbullying, contributing to safer online communities. Experimental results highlight its potential for real-world deployment.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 2 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0802003

Pages

13856-13867

Published

April 7, 2025
Copyright

Open Access

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

How to Cite

Thanuja S, Dr. Priya V, Vanthanadevi S, Yuvanidhi S (%2025). Leveraging Deep Learning for Cyber Bullying Detection on Social Media Platforms: A Holistic Approach to Mitigate Online Harassment. International Journal of Science, Research and Technology , Vol. 8 No. 2 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 13856-13867. https://doi.org/10.15662/IJSRAT.2025.0802003

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