Future of Information and Communication Conference (FICC) 2025
28-29 April 2025
Publication Links
IJACSA
Special Issues
Future of Information and Communication Conference (FICC)
Computing Conference
Intelligent Systems Conference (IntelliSys)
Future Technologies Conference (FTC)
International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 5, 2024.
Abstract: Upholding a secure and accepting digital environment is severely hindered by hate speech and inappropriate information on the internet. A novel approach that combines Convolutional Neural Network with GRU and BERT from Transformers proposed for enhancing the identification of offensive content, particularly hate speech. The method utilizes the strengths of both CNN-GRU and BERT models to capture complex linguistic patterns and contextual information present in hate speech. The proposed model first utilizes CNN-GRU to extract local and sequential features from textual data, allowing for effective representation learning of offensive language. Subsequently, BERT, advanced transformer-based model, is employed to capture contextualized representations of the text, thereby enhancing the understanding of detailed linguistic nuances and cultural contexts associated with hate speech. Fine tuning BERT model using hugging face transformer. To execute tests using datasets for hate speech identification that are made accessible to the public and show how well the method works to identify inappropriate content. By assisting with the continuing efforts to prevent the dissemination of hate speech and undesirable language online, the proposed framework promotes a more diverse and secure digital environment. The proposed method is implemented using python. The method achieves 98% competitive performance compared to existing approaches LSTM and RNN, CNN, LSTM and GBAT, showcasing its potential for real-world applications in combating online hate speech. Furthermore, it provides insights into the interpretability of the model's predictions, highlighting key linguistic and contextual factors influencing offensive language detection. The study contributes to advancing hate speech detection by integrating CNN-GRU and BERT models, giving a robust solution for enhancing offensive content identification in online platforms.
M. Madhavi, Sanjay Agal, Niyati Dhirubhai Odedra, Harish Chowdhary, Taranpreet Singh Ruprah, Veera Ankalu Vuyyuru and Yousef A.Baker El-Ebiary, “Elevating Offensive Language Detection: CNN-GRU and BERT for Enhanced Hate Speech Identification” International Journal of Advanced Computer Science and Applications(IJACSA), 15(5), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01505118
@article{Madhavi2024,
title = {Elevating Offensive Language Detection: CNN-GRU and BERT for Enhanced Hate Speech Identification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01505118},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01505118},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {5},
author = {M. Madhavi and Sanjay Agal and Niyati Dhirubhai Odedra and Harish Chowdhary and Taranpreet Singh Ruprah and Veera Ankalu Vuyyuru and Yousef A.Baker El-Ebiary}
}
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.