Paper Contents
Abstract
The spread of fake news has become a significant issue in the digital age, affecting societal trust and decision-making. This paper presents a machine learning-based approach to fake news detection using the Passive-Aggressive Classifier. By leveraging natural language processing (NLP) for text preprocessing and feature extraction, the system classifies news articles as real or fake. The model demonstrates high efficiency and scalability, making it suitable for real-time applications such as social media moderation. This study highlights the effectiveness of the Passive-Aggressive Classifier for dynamic environments and provides insights into future enhancements to improve detection accuracy and applicability.
Copyright
Copyright © 2025 Gunashekhar . This is an open access article distributed under the Creative Commons Attribution License.
