Fake News Detection Using Natural Language Processing (NLP)

Abstract

Fake news has become one of the most critical challenges in today’s information-driven world. Social media, online news platforms, and instant messaging apps make it easy for misinformation to spread rapidly, often with serious consequences for politics, public health, and society. This report examines how Natural Language Processing (NLP) techniques, supported by Machine Learning (ML) and Deep Learning (DL), can be used to automatically detect fake news. A literature-based review highlights the effectiveness of models such as SVM, Random Forest, LSTM, Bi-LSTM, GRU, and CNN. The study also explores feature extraction techniques like TF-IDF, Word2Vec, and GloVe, alongside the role of dataset diversity and multilingual contexts. A real case study of fake news during the COVID-19 pandemic is discussed to show the real-world impact of misinformation. The findings suggest that while ML models provide efficient solutions, DL approaches offer superior accuracy and contextual understanding, and challenges remain in reproducibility, bias, and multilingual detection.