GETRAL

This is the code for the IEEE TKDE Paper: Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks.

Usage

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)

Requirements

We use Pytorch 1.9.1 and python 3.6. Other requirements are in requirements.txt.

pip install -r requirements.txt

Citation

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}
}

Acknowledge

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.