FAKE NEWS DETECTION

Abstract

Fake news detection has emerged as a critical
research area due to the rapid spread of
misinformation across digital platforms and social
media. This project presents an intelligent system
that leverages machine learning and Natural
Language Processing (NLP) techniques to
automatically classify news articles as real or fake.
The system processes textual data through multiple
stages including data collection, preprocessing,
feature extraction, model training, and
classification. Text preprocessing techniques such
as tokenization, stop-word removal, stemming, and
normalization are applied to improve data quality.
Feature extraction is performed using TF-IDF
vectorization to convert textual content into
numerical representations suitable for machine
learning models. Algorithms such as Naive Bayes,
Logistic Regression, and Support Vector Machines
(SVM) are implemented to achieve accurate
classification. The system is trained on labeled
datasets and evaluated using performance metrics
such as accuracy, precision, recall, and F1-score.
The proposed approach reduces dependency on
manual fact-checking and enhances real-time
detection capability. Additionally, the system is
scalable and can be integrated into web platforms
to assist users in verifying information authenticity.
This solution contributes to minimizing
misinformation, improving public awareness, and
promoting trustworthy information dissemination.
Overall, the project demonstrates an efficient,
reliable, and automated framework for fake news
detection in modern digital environments.