Machine Learning Techniques for Fake News Detection

The rapid proliferation of fake news on digital platforms has emerged as a significant challenge, undermining trust in information and influencing public opinion. To combat this issue, researchers have increasingly turned to machine learning (ML) techniques for automated fake news detection. This paper explores the application of various ML approaches, including supervised, unsupervised, and deep learning models, to identify and classify fake news. Key techniques such as natural language processing (NLP), sentiment analysis, and feature extraction are discussed, highlighting their role in improving detection accuracy. Additionally, the challenges of dataset quality, model interpretability, and real-time detection are addressed. The study concludes that while ML techniques show promise in fake news detection, ongoing advancements in model robustness and adaptability are essential to keep pace with the evolving nature of misinformation.