Advanced Techniques for Fake News Detection on Twitter Using NLP and AI: A Comprehensive Review

This review paper explores the critical aspects of pre-processing, feature extraction, feature selection, and classification in fake news detection on Twitter. Pre-processing involves cleaning text data by removing unwanted elements and normalizing the text. Feature extraction transforms raw text into analysable formats, identifying significant aspects to distinguish between fake and real news. Feature selection focuses on identifying relevant features to reduce dimensionality and noise. Various classification techniques are evaluated for their effectiveness, demonstrating the potential of machine learning in achieving high accuracy. The paper also examines Twitter's role in rapid information dissemination and the challenges posed by fake news. It reviews content-based methods like text analysis, user-based methods such as behaviour and network analysis, hybrid methods integrating multiple features, and the use of external knowledge through fact-checking and knowledge graphs. Addressing the challenges of high information volume, rapid spread, and evasion techniques, the paper concludes that effective fake news detection on Twitter requires advanced analytical techniques and real-time monitoring to manage and mitigate misinformation.