A Multilingual Fake News Detection and Fact-Checking System Using NLP and Machine Learning

This paper presents a machine learning based approach for detecting fake news using natural language processing techniques. The proposed system applies text preprocessing, TF-IDF feature extraction, and supervised classification algorithms such as Logistic Regression, Naive Bayes, and Passive-Aggressive Classifier to classify news articles as real or fake. Experimental results demonstrate that traditional machine learning models can achieve reliable performance on benchmark datasets. The study highlights the effectiveness of lightweight NLP models for fake news detection and can be extended for real-world misinformation analysis.