This model uses a Passive Aggressive Classifier with the parameters of max_iter=50, C=0.5. The dataset has 20,800 different articles with 10413 “Fake” and 10387 “Real”. The test size was 25% and the accuracy was 96.43%. Additionally, the K fold accuracy was 96.28% when cv=5. To test the model further, testing was done with a different dataset. Using this dataset, the accuracy was 76.43%. Limitations: The datasets used were from 2016-2018 based on US politics hence the model works best with US political articles. This website was made using Flask as a backend and HTML, CSS and Java Script for frontend and validation.
https://drive.google.com/drive/folders/1SI3q2nJddkP1wzYJQVFELfPJC9pHzTe9?usp=sharing
The Predict button redirects the user to the Result page.
The article used for this example is from the New York Times: https://www.nytimes.com/live/2021/05/18/world/israel-gaza-updates
Here the classification is outputted.
Mac or Linux
Install VirtualEnv.
python3 -m venv env
source env/bin/activate
At this point make sure to have your virtual environment activated. You should get (env) in you terminal.
pip install -r requirements.txt
At this point make sure to be in your folder that holds the app.py file.
python app.py
Then copy paste the local host with the port in any web browser. It will most likely start with '127.0'.
Hosting this projects takes 4GB ram on a VPS hence making it difficult to host.
If you find the code in the repository useful, please cite it using:
@miscsShloakr2021Fake-News-Classifier,
author = {Shloak Rathod},
title = {An Open Source Implementation of Fake News classification},
year = {2021},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {Available at \url{https://github.com/shloakr/Fake-News-Classifier/}}
}