Fake News Detection System: An Ensemble Approach

Fake news has become a critical challenge in digital communication, threatening information integrity and public discourse, especially in this internet era where misunderstanding can lead to dispute of opinions, conflicts and wars. This research presents a comprehensive machine learning framework for fake news detection using ensemble architecture involving Machine Learning and Deep Learning. By integrating natural language processing, content-based feature extraction, and lexical analysis, a robust detection model was developed. Our methodology leveraged a diverse dataset of 72,134 news articles, applying ensemble learning algorithms to classify potential misinformation. The proposed approach achieved a remarkable 0.97 accuracy. Our findings demonstrate the potential of ensemble techniques in combating digital misinformation, offering a scalable solution for identifying and mitigating fake news across digital platforms.