DBFN-J: A Lightweight and Efficient Model for Hate Speech Detection on Social Media Platforms

Hate speech on social media platforms like YouTube, Facebook, and Twitter threatens online safety and societal harmony. Addressing this global challenge requires innovative and efficient solutions. We propose DBFN-J (DistillBERT-Feedforward Neural Network with Jaya optimization), a lightweight and effective algorithm for detecting hate speech. This method combines DistillBERT, a distilled version of the Bidirectional Encoder Representations from Transformers (BERT), with a Feedforward Neural Network. The Jaya algorithm is employed for parameter optimization, while aspect-based sentiment analysis further enhances model performance and computational efficiency. DBFN-J demonstrates significant improvements over existing methods such as CNN BERT (Convolutional Neural Network BERT), BERT-LSTM (Long Short-Term Memory), and ELMo (Embeddings from Language Models). Extensive experiments reveal exceptional results, including an AUC (Area Under the Curve) of 0.99, a log loss of 0.06, and a balanced F1-score of 0.95. These metrics underscore its robust ability to identify abusive content effectively and efficiently. Statistical analysis further confirms its precision (0.98) and recall, making it a reliable tool for detecting hate speech across diverse social media platforms. By outperforming traditional algorithms in both performance and resource utilization, DBFN-J establishes a new benchmark for hate speech detection. Its lightweight design ensures suitability for large-scale, resource-constrained applications. This research provides a robust framework for protecting online environments, fostering healthier digital spaces, and mitigating the societal harm caused by hate speech.

@article{Janbi2025,
  title     = {DBFN-J: A Lightweight and Efficient Model for Hate Speech Detection on Social Media Platforms},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {1},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nourah Fahad Janbi and Abdulwahab Ali Almazroi and Nasir Ayub},
  doi       = {10.14569/IJACSA.2025.01601128},
  url       = {https://doi.org/10.14569/IJACSA.2025.01601128}
}