Automating Fake News Detection and Content Moderation Using Machine Learning: A Comprehensive Approach

Fake news and toxic online content proliferate across digital platforms, causing harm to the trust of the social structure. In this context, this study focuses on using machine learning techniques to help solve such problems by using automated detection and moderation systems. We will present a framework that combines advanced natural language processing (NLP) methods with supervised learning algorithms to detect misinformation and inappropriate content in various domains. Training and validation rely on publicly available datasets, so model performance can be fully assessed. The paper highlights a comprehensive study on feature extraction, such as sentiment analysis, contextual embedding, and linguistic patterns, and also shows a scalable architecture for real-time content evaluation. The experiments reveal that the developed system correctly identifies fake news from reliable information and can detect violative activities concerning community guidelines. It addresses explainability and the importance of mitigating biases within machine learning models that enhance the prospects for fairness and transparency. Its findings also allow actionable contributions to improved automated content moderation systems toward the more safety-oriented digital ecosystem.