A Machine Learning Models for Classifying Fake and Real News Articles

Abstract — The era where misinformation spreads rapidly across digital platforms, ability to distinguish between authentic and fabricated news has become a critical societal challenge. This project presents a machine learning-based approach to fake and real news detection using natural language processing techniques. Utilizing a labelled dataset comprising 6,335 news articles, the model analyzes both the title and content of each entry to accurately classify them as either “FAKE” or “REAL.” Pre-processing steps, including tokenization, vectorization, and noise removal, was applied to enhance text clarity. Multiple machine learning algorithms were evaluated, with performance measured through accuracy, precision, recall, and F1-score. The results underscore the efficacy of supervised learning techniques in automating the verification of news content, offering a scalable solution to combat the proliferation of misinformation in online media.

Machine learning, Supervised learning, Natural Language Processing (NLP), Text Classification

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