The rapid expansion of digital media platforms has significantly increased the spread of misinformation, posing serious threats to public opinion, political stability, and social harmony. The automated identification of fake news has therefore become a critical research challenge in the fields of machine learning and natural language processing. This paper presents an intelligent and robust fake news detection framework that leverages advanced textual feature extraction and ensemble learning techniques to improve classification performance. The proposed system incorporates comprehensive data preprocessing, including text normalization, stop-word removal, tokenization, and vectorization using TF-IDF representations. Multiple supervised machine learning algorithms such as Logistic Regression, Support Vector Machine (SVM), Random Forest, and Gradient Boosting are trained and evaluated using stratified cross-validation to ensure reliability and generalization. To enhance predictive accuracy and reduce model bias, an ensemble-based voting mechanism is employed. Performance evaluation is conducted using metrics including accuracy, precision, recall, F1-score, and ROC-AUC to address class imbalance and misclassification risks. Experimental results demonstrate that the ensemble framework achieves superior performance compared to individual classifiers, providing a scalable and dependable solution for real-time misinformation detection in digital environments. The proposed approach contributes toward building trustworthy information ecosystems through automated and explainable fake news classification.
Vardhan et al. (Thu,) studied this question.
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