A Comparative Study of Machine Learning Algorithms for Fake News Detection Using NLP Techniques

The rapid expansion of digital media platforms has increased the spread of fake news, resulting in misinformation and social challenges. Manual verification of online news content is inefficient due to the large volume of data. This study proposes a machine learning-based approach for fake news detection using natural language processing techniques. The Fake and Real News dataset obtained from Kaggle is used for experimentation. Text preprocessing is performed to remove noise, followed by feature extraction using the Term Frequency–Inverse Document Frequency (TF-IDF) method. Multiple machine learning classifiers, including Naive Bayes, Logistic Regression, Support Vector Machine, and Random Forest, are implemented and evaluated. Experimental results indicate that Support Vector Machine and Random Forest achieve higher accuracy compared to traditional classifiers. The proposed approach demonstrates the effectiveness of machine learning techniques in automated fake news detection.