Advancing Cyberbullying Detection: A Hybrid Machine Learning and Deep Learning Framework for Social Media Analysis

Bishal Shyam Purkayastha, Md. Musfiqur Rahman, Md. Towhidul Islam Talukdar, Maryam Shahpasand

2025

Abstract

Social media platforms have led to the prevalence of cyberbullying, seriously challenging the mental health of individuals. This research is on how effectively different machine learning and deep learning techniques can detect cyberbullying in online communications. Using two different tweet datasets obtained from Mandalay and Kaggle, we developed a balanced framework for binary classification. This research emphasizes comprehensive data preprocessing: text normalization and class balancing by random oversampling to increase the dataset’s quality. Models used include several traditional machine learning classifiers: Random Forest, Extra Trees, AdaBoost, MLP, and XGBoost, and advanced deep learning architectures such as Bidirectional LSTM, BiGRU, and BERT. These results confirm that deep learning models, especially BERT, yield outstanding performance with an accuracy rate of 92%, hence showing the models’ capability in effectively detecting and preventing cyberbullying through automated detection.

Paper Citation

in Harvard Style

Purkayastha B., Rahman M., Talukdar M. and Shahpasand M. (2025). Advancing Cyberbullying Detection: A Hybrid Machine Learning and Deep Learning Framework for Social Media Analysis. In Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS; ISBN 978-989-758-749-8, SciTePress, pages 348-355. DOI: 10.5220/0013436200003929

in Bibtex Style

@conference{iceis25,
author={Bishal Shyam Purkayastha and Md. Musfiqur Rahman and Md. Towhidul Islam Talukdar and Maryam Shahpasand},
title={Advancing Cyberbullying Detection: A Hybrid Machine Learning and Deep Learning Framework for Social Media Analysis},
booktitle={Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS},
year={2025},
pages={348-355},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013436200003929},
isbn={978-989-758-749-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 27th International Conference on Enterprise Information Systems - Volume 2: ICEIS
TI - Advancing Cyberbullying Detection: A Hybrid Machine Learning and Deep Learning Framework for Social Media Analysis
SN - 978-989-758-749-8
AU - Purkayastha B.
AU - Rahman M.
AU - Talukdar M.
AU - Shahpasand M.
PY - 2025
SP - 348
EP - 355
DO - 10.5220/0013436200003929
PB - SciTePress