Paper Contents
Abstract
The rapid spread of fake news online undermines trust in digital media and misguides public opinion. This paper explores machine learning and natural language processing (NLP) techniques for classifying news as real or fake. Using TFIDF features from article text, classifiers such as Logistic Regression, Nave Bayes, and Random Forest were evaluated. Results show Logistic Regression achieves the best balance of accuracy and efficiency, highlighting the effectiveness of lightweight models for fake news detection.
Copyright
Copyright © 2025 Martin K R. This is an open access article distributed under the Creative Commons Attribution License.
Paper ID:
IJPREMS50900038045
ISSN:
2321-9653
Publisher:
ijprems