Detecting Fake News Using Hybrid Machine Learning Models

The increasing diffusion of misinformation in online media has raised alarm as a significant threat to information credibility and societal trust. The ease of disseminating false information across social media platforms, news websites, and digital forums has led to severe consequences, including political manipulation, financial fraud, and public misinformation. This research outlines a robust strategy for detecting misinformation using Natural Language Processing. To take the detection process a step further, the study analyses the implementation of ensemble models. Ensemble learning combines multiple classifiers to improve generalization and robustness, reducing the likelihood of misclassification and helps the model focus on critical words and phrases that contribute to determining the authenticity of news articles. The operational efficacy of the model is measured exploiting standard evaluation assessment metric fidelity, precision, recall, and F1-score to confirm consistent performance.  Inclusion of ensemble learning further improves classification accuracy by reducing biases inherent in individual models. Future work in this domain can focus on Live Monitoring for Misinformation, multilingual analysis, and incorporation of context-aware models to further refine detection capabilities. By continuously evolving NLP-based approaches, researchers and technology developers can serve an important function in mitigating the effect of misinformation on social dynamics.