AI-Driven Fake News Detection Using NLP and Machine Learning

Abstract

The rapid growth of online news platforms and social media has increased the spread of fake news, leading to
misinformation and public confusion. Detecting fake news manually is time-consuming and inefficient due to the
large volume of digital content. This project proposes an AI-Driven Fake News Detection System using Natural
Language Processing (NLP) and Machine Learning to automatically identify and classify news as real or fake.
The proposed system analyzes textual news content by applying NLP techniques such as tokenization, stopword
removal, and feature extraction using TF-IDF. Machine learning algorithms are trained on labeled datasets to
learn patterns associated with fake and real news. The system provides accurate and fast classification, reducing
human effort and improving reliability.
The experimental results demonstrate that the proposed system effectively detects fake news with satisfactory
accuracy. The system is scalable, cost-effective, and suitable for real-world applications such as online news
platforms and social media monitoring.
Index Terms— Fake News Detection, Artificial Intelligence, Natural Language Processing (NLP), Machine
Learning, Text Classification, TF-IDF, Naive Bayes, Logistic Regression, Text Preprocessing, Feature Extraction,
Tokenization, Stopword Removal, Misinformation Detection, Data Analysis, News Classification.