Abstract: 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.

Abdulaziz A Alzubaidi and Amin A Alawady. “SBERT-Based Stacking Ensemble Model for Fake News Detection”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.11 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161149

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