Fake News Detection Deep Learning

Abstract

1. Introduction

This repository is for Fake News Detection using Deep Learning models

Fake news is widely spread across social network. Therefore, there is a huge demand to debunk fake news. There are many attempts to detect fake news but limited work is about using Deep Learning models. In this project, we aim to build state-of-the-art deep learning models to detect fake news based on the content of article itself.

We get the ground truth data from https://www.kaggle.com/arminehn/rumor-citation/data#. We only use Snopes URLs since the labels of each news were clearly presented. Only "true" or "false" labels were kept. We randomly select 281 true news and 281 false news and crawl the Snope website for additional content.

2. Objective

Fake News Detection using Deep Learning models in Tensorflow

This project demonstrates how HTML, TensorFlow can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• A modern web browser
• VS Code or any code editor
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/nguyenvo09/fake_news_detection_deep_learning.git
cd fake_news_detection_deep_learning

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by nguyenvo09 and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.