Joint Credibility Estimation of News, User, and Publisher via Role-Relational Graph Convolutional Networks
July 1, 2022 ยท View on GitHub
This repository contains the code for the paper "Joint Credibility Estimation of News, User, and Publisher via Role-Relational Graph Convolutional Networks"
Data Sources
FakeNewsNet-PolitiFact dataset can be downloaded using the code provided at https://github.com/KaiDMML/FakeNewsNet
PolitiFact-2021 dataset can be downloaded using the instructions provided at Data/README.md
Content
- Script to generate train-test split :
Code/Utils/five_fold_train_test_split.ipynb - Script to run baseline models for FakeNewsNet-PolitiFact dataset:
Code/Experiments/FakeNewsNet-PolitiFact/Baseline_exp.ipynb - Script to run Role-RGCN model for FakeNewsNet-PolitiFact dataset:
Code/Experiments/FakeNewsNet-PolitiFact/Role_RGCN_exp.ipynb -
- Script to run baseline models for PolitiFact-2021 dataset:
Code/Experiments/PolitiFact-2021/Baseline_exp.ipynb
- Script to run baseline models for PolitiFact-2021 dataset:
- Script to run Role-RGCN model for PolitiFact-2021 dataset:
Code/Experiments/PolitiFact-2021/Role_RGCN_exp.ipynb - Helper files:
Utils/features.py,Models/models.py