<![CDATA[The rapid spread of disinformation and fabricated news across online platforms poses a critical risk to informed public engagement and the foundations of democratic governance. This study examines how well different machine learning techniques can classify fake news, using textual features extracted through the Term Frequency–Inverse Document Frequency (TF-IDF) method. The analysis includes five commonly used algorithms like Logistic Regression, Support Vector Machine (SVM), Naive Bayes, Random Forest, and XGBoost. A publicly accessible dataset containing annotated real and fake news articles served as the basis for training and testing these models. Dataset underwent extensive preprocessing, including tokenization, stopword removal, and TF-IDF vectorization, resulting in a sparse high-dimensional matrix of 5068 documents and 39,978 features. Performance evaluation was based on multiple metrics: train/test accuracy, misclassification rate, false positives/negatives, cross-validation mean score, and execution time. Results showed that SVM and Logistic Regression achieved the highest test accuracy (93.61% and 92.27%, respectively) and exhibited robust cross-validation scores, indicating strong generalization ability. In contrast, Naive Bayes produced faster results but suffered from a high false positive rate and lower accuracy (84.77%). Random Forest and XGBoost demonstrated good predictive power but showed signs of overfitting and moderate misclassification rates. These findings suggest that SVM and Logistic Regression are well-suited for fake news detection in textual datasets using TF-IDF features. While traditional models remain effective, future work may explore deep learning approaches and context-aware language models to enhance detection accuracy across more complex and multilingual datasets. This study contributes to the ongoing efforts to combat misinformation through automated, scalable, and interpretable machine learning techniques.]]>
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