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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: This research paper addresses the critical issue of cyberbullying detection within the realm of social networks, employing a comprehensive examination of various machine learning and deep learning techniques. The study investigates the performance of these methodologies through rigorous evaluation using standard metrics, including Accuracy, Precision, Recall, F-measure, and AUC-ROC. The findings highlight the notable efficacy of deep learning models, particularly the Bidirectional Long Short-Term Memory (BiLSTM) architecture, in consistently outperforming alternative methods across diverse classification tasks. Confusion matrices and graphical representations further elucidate model performance, emphasizing the BiLSTM-based model's remarkable capacity to discern and classify cyberbullying instances accurately. These results underscore the significance of advanced neural network structures in capturing the complexities of online hate speech and offensive content. This research contributes valuable insights toward fostering safer and more inclusive online communities by facilitating early identification and mitigation of cyberbullying. Future investigations may explore hybrid approaches, additional feature integration, or real-time detection systems to further refine and advance the state-of-the-art in addressing this critical societal concern.
Rustam Abdrakhmanov, Serik Muktarovich Kenesbayev, Kamalbek Berkimbayev, Gumyrbek Toikenov, Elmira Abdrashova, Oichagul Alchinbayeva and Aizhan Ydyrys, “Offensive Language Detection on Social Media using Machine Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150557
@article{Abdrakhmanov2024,
title = {Offensive Language Detection on Social Media using Machine Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150557},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150557},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {Rustam Abdrakhmanov and Serik Muktarovich Kenesbayev and Kamalbek Berkimbayev and Gumyrbek Toikenov and Elmira Abdrashova and Oichagul Alchinbayeva and Aizhan Ydyrys}
}
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.