Fake News Detection

This project aims to detect Fake News based on a user query and display the results on a webpage using Flask.

Dataset overview

FakeNewsNet contains 2 datasets collected using ground truths from Politifact and Gossipcop. The minimalistic version of this dataset provided by FakeNewsNet includes the following files:

politifact_fake.csv - Samples related to fake news collected from PolitiFact
politifact_real.csv - Samples related to real news collected from PolitiFact
gossipcop_fake.csv - Samples related to fake news collected from GossipCop
gossipcop_real.csv - Samples related to real news collected from GossipCop

Each of the above CSV files is comma separated file and has the following columns:

id - Unique identifider for each news
url - Url of the article from web that published that news
title - Title of the news article
tweet_ids - Tweet ids of tweets sharing the news. This field is list of tweet ids separated by tab

Installation

Requirements:

Data download scripts are writtern in python and requires python 3.8+ to run.

Install all the libraries in requirements.txt using the following command:

pip install -r requirements.txt

Running Code

Virtual Environment setup:

Create the virtual environment:

> py -3 -m venv venv

Activate the corresponding environment:

> venv\Scripts\activate

Flask setup:

Configure flask environment:

> $env:FLASK_ENV = "development"
> $env:FLASK_APP = "get_results.py"

After the setup, run the following command to launch the flask app on your localhost:

> flask run

References

If you use this dataset, please cite the following papers:

@article{shu2018fakenewsnet,
  title={FakeNewsNet: A Data Repository with News Content,
         Social Context and Dynamic Information for Studying Fake News on Social Media},
  author={Shu, Kai and  Mahudeswaran, Deepak and Wang, Suhang and Lee, Dongwon and Liu, Huan},
  journal={arXiv preprint arXiv:1809.01286},
  year={2018}
}

@article{shu2017fake,
  title={Fake News Detection on Social Media: A Data Mining Perspective},
  author={Shu, Kai and Sliva, Amy and Wang, Suhang and Tang, Jiliang and Liu, Huan},
  journal={ACM SIGKDD Explorations Newsletter},
  volume={19},
  number={1},
  pages={22--36},
  year={2017},
  publisher={ACM}
}

@article{shu2017exploiting,
  title={Exploiting Tri-Relationship for Fake News Detection},
  author={Shu, Kai and Wang, Suhang and Liu, Huan},
  journal={arXiv preprint arXiv:1712.07709},
  year={2017}
}

Fake News Detection on Social Media: A Data Mining Perspective
Exploiting Tri-Relationship for Fake News Detection
FakeNewsTracker
FakeNewsNet

ushashwat/Fake-News-Detection