Fake News Detection Using Natural Language Processing (NLP) and Machine Learning

Fake news has emerged as a critical social issue, with misinformation spreading rapidly across digital platforms and undermining public trust. This study investigates automated fake news detection using Natural Language Processing (NLP) and Machine Learning (ML) techniques to distinguish deceptive from credible news content. It examines traditional classifiers such as Logistic Regression, Naïve Bayes, Support Vector Machines, and Decision Trees alongside deep learning and transformer-based models, as well as hybrid approaches that combine ensemble learning with NLP. Key feature extraction methods, including word embeddings, n-grams, and TF-IDF, are used to convert textual data into effective machine-readable representations. The work also explores network-based analysis and sentiment analysis as alternative strategies for detecting misinformation patterns. Experimental results indicate that combining statistical methods with deep learning substantially improves the accuracy and robustness of fake news detection. By clarifying the strengths and limitations of different approaches, the study provides a foundation for developing scalable, reliable systems to combat misinformation and support information integrity in digital media.