Paper Title
Ensemble Hybrid Deep Learning Framework for Fake News Detection Using RoBERTa and Explainable AI

Abstract
Due to the proliferation of online platforms for communication, fake news has recently spread over the Internet. Fake news appears as a significant cause for concern because of its adverse effect on people's opinions and social behavior. Artificial intelligence algorithms have been used increasingly by researchers and social networking service providers in recent years to combat the spread of false information. However, the usage of political terminology and the significant language similarities between false and authentic news make it challenging to identify fake news. Furthermore, the majority of news sentences are typically short and linguistically identical, making it challenging for machine learning models to discern between fake and factual news. Traditional fake news detection solutions have inadequate performance due to incorrect representation and model architecture.To address these challenges, this study proposes a robust ensemble deep learning framework for fake news detection.For feature extraction, GloVe is used to generate meaningful word embeddings, and to optimize the extracted features, the Firefly Algorithm is employed for feature selection. In this study, the proposed ensemble model performs classification by integrating hybrid architectures that combine Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa) with sequential models, namely RoBERTa-BiLSTM, RoBERTa-LSTM, and RoBERTa-GRU. The predictions from the hybrid deep learning models have been combined using majority voting to increase overall performance.Besides, hyperparameters of LSTM and GRU are selected optimally using the Harris Hawks’ optimization algorithm. To improve credibility and transparency, Explainable AI Shapley Additive Explanations (SHAP) is used to elucidate our suggested model's classification.Our experimental outcomes revealed that the proposed RoBERTa with sequence models performs better than the BERT by 2.5% in accuracy and the XLNet model by 5.35% in accuracy. Keywords - Fake News Detection,SHAP, Firefly Algorithm,Ensemble Approach, Majority Voting, RoBERTa-LSTM, RoBERTa-BiLSTM and RoBERTa-GRU,Harris Hawks optimization algorithm