FaKe : A Cross-Browser Extension For Classifying Filipino Fake News

Cross-browser extension for detecting fake news written in Filipino, powered by machine learning.

Description

This repository hosts a fake news classifier trained partly on a corpus of Filipino articles, Fake News Filipino, adapted from Cruz et al. We have also built a similar corpus named Fake News Filipino 2024. As part of our undergraduate study at the University of the Philippines Visayas, we have built a cross-browser extension that employs our fake news classifier. We utilize Tampermonkey as a wrapper around our extension and Render to host our machine learning model on the cloud as an API. The fake news classifier only works on articles written primarily in Filipino with smatterings of English vernacular. Currently, we have deployed a trained Logistic Regression model that has achieved a top accuracy of ~98% accuracy on our test set. All resources that have been used in this study are available on this repository.

Installation

1. Download and install Tampermonkey for your browser of choice.
2. Download the latest release of the extension from releases.
3. Extract the zip file and open script.js with Notepad or your plaintext editor of choice.
4. Open the Tampermonkey dashboard.
5. Click the + button and it will open a new userscript.
6. Copy and paste the contents of script.js over the userscript, replacing the old contents.
7. Click file and then save.

Usage

1. Pin Tampermonkey to your browser's address bar.
2. Once pinned, click the Tampermonkey icon.
3. Under Fake News Detector, click Detect Fake News.
4. Wait for the system to process the article you're reading.
5. Profit!

The FaKe Team

1. Rene Andre Jocsing Lead Programmer, Project Manager, Writer
2. Chancy Ponce de Leon Programmer, Data Analyst
3. Cobe Austin Lupac Presenter, Programmer
4. Ron Gerlan Naragdao Programmer

Contributing

If you wish to contribute to the project, please open a pull request.

Resources

@inproceedings{cruz2020localization,
  title={Localization of Fake News Detection via Multitask Transfer Learning},
  author={Cruz, Jan Christian Blaise and Tan, Julianne Agatha and Cheng, Charibeth},
  booktitle={Proceedings of The 12th Language Resources and Evaluation Conference},
  pages={2596--2604},
  year={2020}
}

@article{evaluating2019cruz,
  title={{Evaluating Language Model Finetuning Techniques for Low-resource Languages}},
  author={Cruz, Jan Christian Blaise and Cheng, Charibeth},
  journal={arXiv preprint arXiv:1907.00409},
  year={2019}
}

@article{imperial2021application,
  author={Imperial, J. M. and Ong, E.},
  title={Application of Lexical Features Towards Improvement of Filipino Readability Identification of Children's Literature},
  journal={arXiv preprint},
  eprint={2101.10537},
  year={2021}
}

@article{imperial2021diverse,
  author={Imperial, J. M. and Ong, E.},
  title={Diverse Linguistic Features for Assessing Reading Difficulty of Educational Filipino Texts},
  journal={arXiv preprint},
  eprint={2108.00241},
  year={2021}
}

@inproceedings{imperial2020exploring,
  author={Imperial, J. M. and Ong, E.},
  title={Exploring Hybrid Linguistic Feature Sets To Measure Filipino Text Readability},
  booktitle={2020 International Conference on Asian Language Processing (IALP)},
  pages={175-180},
  year={2020},
  organization={IEEE}
}