Fa Know

Abstract

1. Introduction

FaKnow is designed for reproducing and developing fake news detection algorithms.

2. Objective

FaKnow is designed for reproducing and developing fake news detection algorithms.

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

3. Key Features / Modules

• Unified Framework: provide a unified interface to cover a series of algorithm development processes, including data processing, model developing, training and evaluation
• Generic Data Structure: use json as the file format read into the framework to fit the format of the data crawled down, allowing the user to customize the processing of different fields
• Diverse Models: contains a number of representative fake news detection algorithms published in conferences or journals during recent years, including a variety of content-based and social context-based models
• Convenient Usability: pytorch based style makes it easy to use with rich auxiliary functions like loss visualization, logging, parameter saving
• Great Scalability: users just focus on the exposed API and inherit built-in classes to reuse most of the functionality and only need to write a little code to meet new requirements

4. Technology Stack

5. System Requirements

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

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/NPURG/FaKnow.git
cd FaKnow

1. from pip
2. from source

pip install faknow

git clone https://github.com/NPURG/FaKnow.git && cd FaKnow
pip install -e . --verbose

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 NPURG 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.