AI-Based Fake News Detection Using Natural Language Processing

Abstract

ABSTRACT The rapid proliferation of fake news and misinformation across digital platforms poses a serious threat to democratic processes, public health, and social stability. This research paper presents a comprehensive study of an AI-based fake news detection system leveraging Natural Language Processing (NLP) techniques and multiple machine learning algorithms. The proposed system analyzes textual content, linguistic patterns, writing style, and sentiment cues to automatically classify news articles as real or fake. Several classification algorithms — including Naive Bayes, Logistic Regression, Support Vector Machine (SVM), Random Forest, Decision Tree, and Long Short-Term Memory (LSTM) neural networks — are implemented and compared. Feature extraction is performed using TF-IDF and Bag of Words representations. The system is evaluated using standard metrics: Accuracy, Precision, Recall, F1-Score, and Confusion Matrix. Experimental results demonstrate that deep learning models, particularly LSTM, achieve superior contextual understanding, while traditional models offer faster inference. The paper also discusses current challenges such as sarcasm detection, multilingual content, and computational constraints, along with future directions including real-time monitoring and transformer-based architectures. This work contributes significantly to the broader effort of combating misinformation through technological innovation and provides a comprehensive roadmap for future research in this critical area. Keywords: Fake News Detection, Natural Language Processing, Machine Learning, Deep Learning, LSTM, TF-IDF, Text Classification, Misinformation, Social Media