This is the code for the IEEE TKDE Paper: Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks.
We utilize two widely used datasets.
- Snopes: http://resources.mpi-inf.mpg.de/impact/dl_cred_analysis/Snopes.zip
- PolitiFact: http://resources.mpi-inf.mpg.de/impact/dl_cred_analysis/PolitiFact.zip
You can run the commands below to train our model on Snopes Dataset.
python MasterFC/master_mac.py --dataset="Snopes" \
--cuda=1 \
--fixed_length_left=30 \
--fixed_length_right=100 \
--log="logs/getral" \
--loss_type="cross_entropy" \
--batch_size=32 \
--num_folds=5 \
--use_claim_source=0 \
--use_article_source=1 \
--path="../formatted_data/declare/" \
--hidden_size=300 \
--epochs=100 \
--num_att_heads_for_words=5 \
--num_att_heads_for_evds=2 \
--gnn_window_size=3 \
--lr=0.0001 \
--gnn_dropout=0.2 \
--seed=123656 \
--alpha=0.5 \
--gsl_rate=0.7
You can also simply run the bash script.
sh run_snopes.sh
or
sh run_politifact.sh (on the PolitiFact dataset)
We use Pytorch 1.9.1 and python 3.6. Other requirements are in requirements.txt.
pip install -r requirements.txt
Please cite our paper if you use the code:
@article{wu2022adversarial,
title={Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks},
author={Wu, Junfei and Xu, Weizhi and Liu, Qiang and Wu, Shu and Wang, Liang},
journal={arXiv preprint arXiv:2210.05498},
year={2022}
}
The general structure of our codes inherits from the open-source codes of MAC, we thank them for their great contribution to the research community of fake news detection.
