A Study on AI Techniques for Fake News Detection

Fake news has become a major problem in the digital world due to the rapid growth of social media and online communication platforms. False or misleading information spreads quickly and influences public opinion, politics, health, and society. This research presents a multi-dimensional analysis of AI-based fake news detection systems using Machine Learning, Deep Learning, and Natural Language Processing (NLP) techniques. The study analyzes textual content, source credibility, user behavior, social media interactions, and multimedia information to classify news as real or fake. Various AI models such as Naive Bayes, Random Forest, Support Vector Machine (SVM), CNN, RNN, and LSTM are discussed and compared. The results show that combining multiple dimensions improves the accuracy and reliability of fake news detection systems. The research highlights the importance of Artificial Intelligence in reducing misinformation and promoting trustworthy information in the digital age.