Fake news detection has become a major challenge in the digital era due to the rapid spread of misinformation through social media and online platforms. The increasing availability of unverified content makes it difficult to identify trustworthy information, leading to serious social, political, and economic consequences. This paper presents a machine learning-based approach for detecting fake news using the Random Forest algorithm. The proposed system applies Natural Language Processing (NLP) techniques such as text preprocessing, tokenization, stop-word removal, and stemming to clean and prepare textual data. Feature extraction is performed using Term Frequency–Inverse Document Frequency (TF-IDF) to convert text into numerical form suitable for model training. The Random Forest classifier is then used to classify news articles as real or fake based on learned patterns. The model is evaluated using performance metrics such as accuracy, precision, recall, and F1-score. Experimental results show that the Random Forest algorithm provides high accuracy and robustness compared to other traditional models. The system is efficient in handling large datasets and reduces the risk of overfitting. This approach can be applied in real-world applications such as social media monitoring and news verification platforms. The study highlights the importance of machine learning techniques in combating misinformation. Future improvements may include the use of deep learning models and real-time detection systems for enhanced performance.
NARESH KUMAR C Mr.P. RAJAPANDIAN (Sat,) studied this question.