The Role of NLP In Fake News Detection and Misinformation Mitigation

In the era of rapid digital communication, the spread of fake news has emerged as a global challenge, influencing public opinion and undermining trust in information systems. This review explores the pivotal role of Natural Language Processing (NLP) in detecting fake news and mitigating misinformation. Various approaches integrate NLP techniques with machine learning (ML) and deep learning (DL) architectures to enhance detection accuracy and robustness. Commonly used text representation methods include TF-IDF, Word2Vec, GloVe, and BERT, often supported by syntactic and semantic features such as POS tagging, named entity recognition (NER), stylometry, and sentiment analysis. Advanced architectures like CNN-RNN hybrids, dual BERT models, and capsule networks have demonstrated high effectiveness, with performance metrics reaching up to 99.8% on benchmark datasets. Further strategies such as ensemble learning, stance detection, adversarial robustness, and the incorporation of external verification tools have been shown to improve credibility assessment. While many models achieve high accuracy in controlled environments, challenges persist in cross-domain generalization, multilingual adaptability, and ethical transparency. This review highlights the critical contributions of NLP in combating misinformation and recommends future systems to leverage multimodal data, real-time responsiveness, and explainable AI for more resilient and trustworthy detection frameworks.