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DOI: 10.14569/IJACSA.2024.0151018
PDF

Cyberbullying Detection on Social Networks Using a Hybrid Deep Learning Architecture Based on Convolutional and Recurrent Models

Author 1: Aigerim Altayeva
Author 2: Rustam Abdrakhmanov
Author 3: Aigerim Toktarova
Author 4: Abdimukhan Tolep

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 10, 2024.

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Abstract: This research paper explores the development and efficacy of a hybrid deep learning architecture for cyberbullying detection on social media platforms, integrating Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. By leveraging the strengths of both CNNs and LSTMs, the model aims to enhance the accuracy and sensitivity of detecting cyberbullying incidents. The study systematically evaluates the performance of the proposed model through a series of experiments involving a diverse dataset derived from various social media interactions, categorized by sentiment and type of bullying. Results indicate that while the model achieves high accuracy in identifying cyberbullying, challenges such as overfitting and the need for better generalization to unseen data persist. The paper also discusses ethical considerations and the potential for bias in automated monitoring systems, stressing the importance of ethical AI practices in social media governance. The findings underscore the complexity of automated cyberbullying detection and highlight the necessity for advanced machine learning techniques that are robust, scalable, and aligned with ethical standards. This study contributes to the broader discourse on the application of artificial intelligence in enhancing digital safety and advocates for a multidisciplinary approach to address the socio-technical challenges posed by cyberbullying in the digital age.

Keywords: Cyberbullying detection; deep learning; CNN; LSTM; social media monitoring; sentiment analysis; digital safety

Aigerim Altayeva, Rustam Abdrakhmanov, Aigerim Toktarova and Abdimukhan Tolep, “Cyberbullying Detection on Social Networks Using a Hybrid Deep Learning Architecture Based on Convolutional and Recurrent Models” International Journal of Advanced Computer Science and Applications(IJACSA), 15(10), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151018

@article{Altayeva2024,
title = {Cyberbullying Detection on Social Networks Using a Hybrid Deep Learning Architecture Based on Convolutional and Recurrent Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151018},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151018},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {10},
author = {Aigerim Altayeva and Rustam Abdrakhmanov and Aigerim Toktarova and Abdimukhan Tolep}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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