Towards Accurate Fake News Detection: Evaluating Machine Learning Approaches and Feature Selection Strategies

Abstract

The rapid spread of fake news in the digital age poses significant challenges, necessitating effective detection methods. This study presents a comprehensive evaluation of various ensemble and machine learning classifiers, combined with different feature selection techniques, to improve the accuracy and reliability of the detection of fake news. Using the TruthSeeker dataset, this research examines feature selection methods such as Recursive Feature Elimination (RFE), SelectKBest, Principal Component Analysis (PCA), and Genetic Algorithms (GA), analyzing their impact on model performance. Key metrics such as accuracy, precision, recall, F1 score, and AUC-ROC were used to assess the effectiveness of each classifier. The results reveal that ensemble methods, particularly Random Forest (RF) and Gradient Boosting, demonstrate superior performance, achieving high accuracy and AUC-ROC scores. Moreover, feature selection techniques like RFE and SelectKBest significantly improve model outcomes by optimizing the feature set, while PCA is less effective in this context. This study highlights the importance of integrating robust classifiers with optimal feature selection methods to improve the efficacy of fake news detection systems.