Title: Fake News Detection Using Machine Learning
Description:
Fake news detection has emerged as a captivating research area for both computer scientists and social scientists. The proliferation of false information on social media platforms has had a profound impact on society. With the vast amount of information available from various sources such as Facebook, WhatsApp, and Twitter, it has become increasingly challenging to develop effective methods for identifying fake news. In this project, we propose a state-of-the-art machine learning approach that assesses the authenticity of articles based on the Uniform Resource Locator (URL) provided by the user. We employ popular machine learning techniques, including Long Short-Term Memory (LSTM), Random Forest (randomtree), Random Forest (decision tree), Decision Tree, and Neural Network, to determine the reliability of news content. Our model leverages human-curated data from http://opensources.co, which contains over 700 fake news websites and about 20 reliable news sources, serving as a foundation for defining credible sources.
This project aligns with IEEE Paper standards, making it an exceptional final year engineering project for computer science students.
Components provided:
Join us for this cutting-edge project that addresses the crucial challenge of fake news detection using machine learning. We offer this project at an affordable and competitive price.