An AI-Powered Framework for Fake News Detection using Natural Language Processing,

The proliferation of fake news has become a significant global challenge, undermining public trust and influencing societal discourse. Traditional manual verification processes are inefficient and often fail to keep pace with the rapid dissemination of misinformation. This research presents an AI-based framework for automated fake news detection leveraging Natural Language Processing (NLP) and machine learning. The system employs a multi-faceted approach, integrating various NLP techniques for feature extraction and utilizing a combination of machine learning classifiers, including Random Forest, Support Vector Machines (SVM), and Convolutional Neural Networks (CNNs). The methodology includes comprehensive data preprocessing, feature engineering, and a comparative analysis of model performance. The findings demonstrate the system’s high accuracy in classifying news articles as genuine or fake, highlighting the potential of AI to provide a scalable and efficient solution to combat misinformation and restore trust in digital media.