Efficient Misinformation Detection on Twitter: A Hybrid Approach Using Machine Learning and Bayesian Optimization with Hyperband

The increasing spread of misinformation on Twitter necessitates effective classification models to distinguish between real and fake content. This research explores the performance of various machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), Random Forest (RF), and K-Nearest Neighbors (KNN), for classifying Twitter data. To enhance model accuracy and efficiency, multiple hyperparameter optimization techniques, such as Grid Search, Random Search, Bayesian Optimization, and Genetic Algorithm, are employed. A novel Bayesian Optimization with Hyperband (BOHB) approach is proposed to optimize classification performance while reducing computational cost. Experimental results demonstrate that SVM achieves the highest accuracy of 99%, outperforming other models across key performance metrics. The findings highlight the effectiveness of BOHB in improving misinformation detection, providing a robust and scalable solution for enhancing social media content verification.