Authors: Shweta Arakeri, Dayanand G Savakar, Anjali Deshapande
Abstract: In today’s digital era, information spreads rapidly through social media and online platforms. However, this convenience has led to the rise of misinformation, commonly referred to as fake news. This paper presents a machine learning-based approach to detect fake news articles by analyzing text content using Natural Language Processing (NLP) techniques. The system preprocesses data, extracts features through TF-IDF vectorization, and classifies news using multiple algorithms such as Logistic Regression, Decision Tree, Gradient Boosting, and Random Forest. The project is implemented using a Flask web application to make the tool user-friendly and accessible. The results demonstrate that the ensemble models provide high accuracy and reliability in identifying misinformation
DOI: http://doi.org/10.5281/zenodo.17368296
