Fake News Detection using NLP and Deep Learning

Abstract The rapid growth of online news and social media platforms has led to an increased spread of misinformation and fake news, posing significant social, political, and economic challenges. Traditional manual fact-checking approaches are insufficient to handle the vast amount of digital content generated daily. To address this, Natural Language Processing (NLP) combined with deep learning techniques provides an automated and effective solution for detecting fake news. This study explores various deep learning models, such as Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), to classify news articles as real or fake. Using text preprocessing, feature extraction, and contextual embeddings, the proposed models aim to capture linguistic patterns, semantic meaning, and contextual dependencies within news content. Experimental results on benchmark datasets demonstrate that deep learning methods achieve superior accuracy and robustness compared to traditional machine learning approaches. This work highlights the potential of NLP-driven deep learning systems in combating misinformation, thereby contributing to the development of reliable and trustworthy digital information ecosystems. Keywords: Natural Language Processing, Deep Learning, LSTM, CNN, Machine Learning.