Cyberbullying Detection on Social Networks Using a Hybrid Deep Learning Architecture Based on Convolutional and Recurrent Models
Aigerim Bakatkaliyevna Altayeva, Rustam Abdrakhmanov, Aigerim Toktarova, Abdimukhan Tolep · International Journal of Advanced Computer Science and Applications · 2024
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.