Fake news detection on social media using triple-attention mechanism optimized by advanced tailor optimization algorithm

Problem: While the phenomenal expansion of social media is reshaping the ways through which information is disseminated, such platforms also liberally facilitate the rampant propagation of fake news, which has serious implications on public opinion, democratic processes, and social well-being. Traditional fake news detection techniques as rule-based systems and conventional machine learning models are poles apart from the speed, variety, and virtual processing of social media content and mostly are characterized by low accuracy, poor generalization, and inability to capture multimodal cues. Method: In order to accommodate this contention, a new deep learning mechanism based on the Triple-attention Mechanism optimized by the Advanced Tailor Optimization Algorithm is proposed in this study. Triple-attention Mechanism perceives, estimates, and considers all three most important dimensions for generating news content-the semantic textual part, contextual features, and user activities-generating more all-embracing and context-aware analysis. Advanced Tailor Optimization Algorithm alters dynamically the learning rate and transmission other hyperparameters at the training phase, thereby enhancing convergence, stability, and generalization of the model from adaptive exploration and exploitation. Results: The model is evaluated on four benchmark datasets-FakeNewsNet, LIAR, PolitiFact, and GossipCop-using extensive performance metrics. The experimental findings outdid those of other evaluations, achieving a rarity of accuracy values of 93, 88, 91, and 90; F1-scores of 93, 88, 91, and 90; specificity values of 95, 92, 93, and 91; and sensitivity scores of 94, 89, 92, and 91. This consistent performance also excelled against six cutting-edge baseline models, with respect to all metrics, including precision, Kappa score, Matthew’s correlation coefficient, Dice Similarity Coefficient, and Intersection over Union. Conclusion: These findings establish the fact that the TAM-ATOA framework is efficient and robust in detecting fake news across various domains. The resulting framework, which integrates multi-dimensional attentions with a new adaptive optimization strategy, develops a solution that is more accurate, scalable, and reliable in the battle against misinformation in real-life social media environments.