International Journal of Science, Engineering and TechnologyAuthors: Namrata D. Ghuse, Rupesh Borse
Abstract: The exponential growth of digital information has created an unprecedented challenge in combating fake news, which threatens democratic processes, public health, and social stability. Traditional manual fact-checking mechanisms are fun-damentally inadequate to address the massive scale and velocity of misinformation spread across online platforms. This research presents FakeNewsDetect, an advanced automated system that leverages synergistic Artificial Intelligence (AI) and Machine Learning (ML) techniques for robust fake news classification. Our approach implements a multi-layered analytical framework combining Natural Language Processing (NLP) for deep content analysis with a novel hybrid ensemble model for superior classifi-cation performance. The system extracts comprehensive linguistic features including sentiment patterns, stylistic markers, semantic relationships, and credibility indicators. The core innovation lies in our hybrid ensemble architecture that strategically integrates Logistic Regression, Support Vector Machines (SVM), and Long Short-Term Memory (LSTM) networks through an optimized soft voting mechanism. Extensive experimentation on diverse datasets demonstrates that our proposed model achieves state-of-the-art performance with 95.7% accuracy, 96% precision, 95.5% recall, and 95.7% F1-score, significantly outperforming individual baseline models and existing approaches.
