The spread of misinformation has become a serious global concern, impacting public trust and information integrity. This study investigates the use of advanced machine learning techniques to detect fraudulent news, utilizing a dataset containing both legitimate and false news articles. Preprocessing techniques such as text cleaning and TF-IDF vectorization enhance data quality and model efficiency. Five machine learning algorithms—Random Forest, Support Vector Machine (SVM), Neural Networks, Logistic Regression, and Naïve Bayes—are evaluated based on accuracy, precision, recall, and F1-score. The Random Forest Classifier achieves the highest accuracy of 99.95%, demonstrating superior reliability in distinguishing fake news from authentic articles. While SVM and Neural Networks also perform well, Logistic Regression and Naïve Bayes, though computationally efficient, show relatively lower effectiveness. This research underscores the significance of ensemble models and advanced preprocessing in developing robust fake news detection systems, offering valuable insights for automated misinformation mitigation strategies.
Fake news detection, Machine learning, Text classification, Natural language processing, Misinformation prevention
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