SBERT-Based Stacking Ensemble Model for Fake News Detection

Fake news has become a significant global challenge, affecting public opinion, social dynamics, and decision-making processes. Detecting fabricated news accurately and efficiently remains a challenging task due to the diversity of content, writing styles, and subtle semantic nuances. In this study, we propose a stacking ensemble model that uses SBERT-based semantic embeddings to improve the detection of fake news. The model integrates several machine-learning classifiers with a meta-learner to enhance robustness and predictive reliability. Experiments on the WELFake dataset show that the proposed model achieves 92.74% accuracy, a 93.01% F1-score, and a 97.93% ROC-AUC in classifying fake and real news. These results demonstrate the model’s effectiveness and suggest its potential for broader application across different languages and news domains.

@article{Alzubaidi2025,
  title     = {SBERT-Based Stacking Ensemble Model for Fake News Detection},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Abdulaziz A Alzubaidi and Amin A Alawady},
  doi       = {10.14569/IJACSA.2025.0161149},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161149}
}