Cof CED

Abstract

1. Introduction

A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection is accepted by COLING 2022. CofCED is an explainable method proposed by this paper. We present the first study on explainable fake news detection directly utilizing the wisdom of crowds (raw reports), alleviating the dependency on fact-checked reports.

The codes and LIAR-RAW, RAWFC datasets have been released!

We constructed two realistic datasets, i.e., RAWFC and LIAR-RAW, consisting of raw reports for each claim.

2. Objective

COLING 2022: A Coarse-to-fine Cascaded Evidence-Distillation Neural Network for Explainable Fake News Detection.

This project demonstrates how Python, Deep Learning 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.

• 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/Nicozwy/CofCED.git
cd CofCED

conda create -n fact22 python=3.8
source activate fact22
conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=11.3 -c pytorch
pip install transformers pandas==1.1.2 tqdm==4.50.0 nltk==3.5 rouge-score==0.0.4 sklearn
pip install sentence_transformers   # for evaluation
pip install torch>=1.8

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 Nicozwy and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.