Fake News Identification Using Hybrid Transformer Ensemble Approach

Abstract

The rapid spread of misinformation across digital platforms has made fake news detection a critical challenge, as it can influence public opinion, disrupt social stability, and reduce trust in credible information sources. Manual verification is no longer feasible at scale due to the large volume of content generated daily. Existing approaches have explored hybrid architectures combining transformer-based models such as Bidirectional Encoder Representations from Transformers (BERT) with sequential models like Long Short-Term Memory (LSTM) for fake news classification; however, such approaches may have limitations in capturing the diverse linguistic, contextual, and structural patterns present in textual data. To address this limitation, this paper proposes a hybrid transformer-based ensemble model for automated fake news identification using the FakeNewsNet dataset. The proposed system integrates BERT with LSTM for contextual and sequential learning, Robustly Optimized BERT Pretraining Approach (RoBERTa) for improved textual representation, and Light Gradient Boosting Machine (LightGBM) for learning statistical patterns from textual features. Individual models provide strong baseline performance, while the ensemble combines their predictions using a weighted strategy to improve overall accuracy and robustness. Experimental results show that the ensemble model achieves an accuracy of approximately 93%, outperforming the individual constituent models. The system can be applied in real-time news verification platforms to assist users, journalists, and fact-checkers in identifying misleading information more effectively