Detection and Classification of Fake News Using Natural Language Processing and Deep Learning

The exponential growth of digital media platforms and online information dissemination has significantly amplified the spread of fake news, posing serious threats to public trust, democratic processes, social harmony, and informed decisionmaking. Fake news is intentionally designed to mislead readers by mimicking the linguistic structure, narrative style, and presentation of legitimate journalism, making its automated detection a complex and evolving challenge. In recent years, Natural Language Processing (NLP) and deep learning techniques have emerged as powerful tools for addressing this problem by enabling large-scale, automated analysis of textual content. This review paper presents a comprehensive synthesis of contemporary research on fake news detection, with particular emphasis on NLP-driven feature extraction and deep learningbased classification models. Drawing upon dissertation-based empirical insights and recent scholarly contributions, the review examines linguistic characteristics of fake news, traditional and deep learning-based detection approaches, word embedding techniques, evaluation strategies, and ethical considerations. The analysis highlights the effectiveness of sequential models such as Long Short-Term Memory networks in capturing contextual and narrative inconsistencies inherent in deceptive content. Furthermore, the review identifies persistent challenges related to generalization, dataset bias, interpretability, and real-world deployment. The paper concludes that NLP and deep learning-based fake news detection systems represent a robust and scalable solution for combating misinformation, provided they are developed with methodological rigor, ethical responsibility, and human oversight.