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.