Automated Content Verification Using Machine Learning Models

The rapid spread of false and misleading information across the Internet, social media, and online news platforms has become a major challenge in the digital era. Such misinformation influences public opinion, disrupts social harmony, and undermines trust in media. This study proposes an automated content verification system using machine learning (ML) models to classify online content as real or fake. The research integrates Natural Language Processing (NLP) techniques, supervised learning algorithms, and feature engineering methods. Multiple ML models are implemented and evaluated to compare their effectiveness. Experimental results show that the Support Vector Machine (SVM) model achieved the highest accuracy among the tested models. The proposed framework demonstrates the potential of ML-based systems to support large-scale fact-checking and curb the spread of misinformation in digital media