Hybrid Op-timization

The proliferation of fake news, characterized by the dissemination of inaccurate information to deceive audiences, has become a pressing concern in recent times. Traditional approaches to phony news detection, often focused on analyzing Twitter content, are susceptible to noise and variations in input sequences, leading to suboptimal performance. To address these challenges, this study proposes a novel method called Multi-Head Attention-Hierarchical Bidirectional Long Short-Term Memory (MHA-HBiLSTM) Networks. Our approach involves two phases: training and testing, wherein we employ tweet pre-processing techniques such as stemming, punctuation removal, stop-word elimination, URL handling, and Twitter control removal. Features are represented using the Glove word embedding technique for experimental evaluation and comparison. The MHA-HBiLSTM model integrates multi-head attention and hierarchical concepts, allowing meaningful information extraction from Twitter data. Notably, our model utilizes dual-level attention mechanisms and a hierarchical structure, reflecting the inherent hierarchy in documents and prioritizing key material during document representation. The effectiveness of the proposed MHA-HBiLSTM algorithm is evaluated using the Whale & Multi-Verse (W-MVO) Optimizer approach, with tests conducted on Kaggle and FakeNewsNet datasets. Comparative analysis with traditional machine learning approaches and deep learning models demonstrates the superior performance of the MHA-HBiLSTM approach in fake news detection.