FAKE NEWS DETECTION SYSTEM USING FEATURED-BASED OPTIMIZED MSVM CLASSIFICATION

Abstract

The rapid spread of misinformation and deceptive content across online platforms has become a pressing concern in today's digitally connected world. Fake news can influence public opinion, disrupt democratic processes, and cause widespread panic or confusion. In response to this challenge, this project proposes a robust and intelligent fake news detection system utilizing a featurebased optimized Multi-class Support Vector Machine (MSVM) classification approach. The system extracts a rich set of features from news articles, including lexical patterns, syntactic structures, semantic representations, and contextual cues, ensuring a comprehensive understanding of the input text. These features are then optimized using advanced selection techniques to enhance model performance and reduce computational complexity. The MSVM classifier is trained to identify not only binary outcomes (fake or real) but also multiple categories such as satire, partially true, and misleading content, offering more nuanced and accurate classifications. This hybrid approach improves detection accuracy, scalability, and adaptability across different data sources and languages. By integrating machine learning with optimized feature engineering, the system presents a powerful solution for combating the spread of fake news and can be deployed in realtime environments such as social media platforms, fact-checking tools, and news monitoring applications. The proposed method addresses limitations of existing models and significantly contributes to the advancement of intelligent misinformation detection systems.