Identifying Fake News Using NLP and Supervised Learning for Attribution Analysis

This project addresses the prevalent issue of fake news by classifying articles according to their reliability using natural language processing algorithms. To distinguish between authentic and fraudulent information, the model employs classifiers such as Naive Bayes and SVM by examining in-article attribution patterns. The algorithm recognizes language clues as markers of disinformation, including overstated assertions and a dearth of reliable sources. To prevent the spread of false information, this strategy encourages content monitoring and can be included in social media platforms. The system's real-time processing allows users to make well-informed decisions regarding the credibility of news sources, thereby facilitating the spread of reliable information.