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CBDS-ConvNet: A Cyber-Bullying Detection Model using Convolutional Neural Network

by Ayodeji O. Akinwumi, Ayokunle O. Ige, Joy R. Obafemi, Olatunde D. Akinrolabu, Bolu O. Akingbesote
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Communications on Applied Electronics
Foundation of Computer Science (FCS), NY, USA
Volume 7 - Number 40
Year of Publication: 2025
Authors: Ayodeji O. Akinwumi, Ayokunle O. Ige, Joy R. Obafemi, Olatunde D. Akinrolabu, Bolu O. Akingbesote
10.5120/cae2025652905

Ayodeji O. Akinwumi, Ayokunle O. Ige, Joy R. Obafemi, Olatunde D. Akinrolabu, Bolu O. Akingbesote . CBDS-ConvNet: A Cyber-Bullying Detection Model using Convolutional Neural Network. Communications on Applied Electronics. 7, 40 ( Jan 2025), 11-21. DOI=10.5120/cae2025652905

@article{ 10.5120/cae2025652905,
author = { Ayodeji O. Akinwumi, Ayokunle O. Ige, Joy R. Obafemi, Olatunde D. Akinrolabu, Bolu O. Akingbesote },
title = { CBDS-ConvNet: A Cyber-Bullying Detection Model using Convolutional Neural Network },
journal = { Communications on Applied Electronics },
issue_date = { Jan 2025 },
volume = { 7 },
number = { 40 },
month = { Jan },
year = { 2025 },
issn = { 2394-4714 },
pages = { 11-21 },
numpages = {9},
url = { https://www.caeaccess.org/archives/volume7/number40/cbds-convnet-a-cyber-bullying-detection-model-using-convolutional-neural-network/ },
doi = { 10.5120/cae2025652905 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-01-23T00:57:33.201263+05:30
%A Ayodeji O. Akinwumi
%A Ayokunle O. Ige
%A Joy R. Obafemi
%A Olatunde D. Akinrolabu
%A Bolu O. Akingbesote
%T CBDS-ConvNet: A Cyber-Bullying Detection Model using Convolutional Neural Network
%J Communications on Applied Electronics
%@ 2394-4714
%V 7
%N 40
%P 11-21
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In recent years, the increasing reliance on the internet and the integration of social media into daily life have led to significant advancements in various aspects of human activities. However, these developments have also facilitated unethical behaviors, with cyberbullying emerging as a critical concern. Traditional machine learning models for cyberbullying detection face challenges such as vulnerabilities to adversarial attacks and difficulty capturing nuanced or complex contextual information, often resulting in misclassifications. To address these limitations, this research introduces CBDS-ConvNet, a Convolutional Neural Network-based model designed for real time cyberbullying detection and prevention. The model is structured into five key layers: Data Collection, Data Preprocessing, Training, Cyberbullying Detection, and Performance Evaluation. Data from platforms such as Mendeley, Kaggle, and GitHub were utilized, with preprocessing ensuring the text data was clean and suitable for training. The model achieved an accuracy of 77.65%, precision of 56.26%, recall of 63.86%, and an F1 score of 60.20%, outperforming some other machine learning approaches. To further evaluate the robustness of the developed model, it was tested on a synthesized dataset, achieving an accuracy of 91%, precision of 89%, recall of 81%, and an F1 score of 85%. This research shows the capacity of CNNs in tackling the dynamic and complex nature of social media interactions. By enabling real-time cyberbullying detection, the CBD-ConvNet system provides a robust framework for safer online environments, thereby advancing research efforts in the field of cyberbullying prevention.

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Index Terms

Computer Science
Information Sciences
Machine Learning
Deep Learning
Cybersecurity
Pattern Recognition
Social Media Analysis

Keywords

Cyberbullying Detection Convolutional Neural Networks (CNN) Real-time Detection Text Classification Online Safety

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