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.DS_Store (6148, 2022-04-21)
Evaluation (0, 2022-04-21)
Evaluation\.DS_Store (6148, 2022-04-21)
Evaluation\mzEvaluator.py (1734, 2022-04-21)
Fitting (0, 2022-04-21)
Fitting\.DS_Store (6148, 2022-04-21)
Fitting\FittingFC (0, 2022-04-21)
Fitting\FittingFC\.DS_Store (6148, 2022-04-21)
Fitting\FittingFC\char_man_fitter_query_repr1.py (34859, 2022-04-21)
Fitting\FittingFC\declare_fitter.py (23182, 2022-04-21)
Fitting\FittingFC\multi_level_attention_composite_fitter.py (21917, 2022-04-21)
Fitting\densebaseline_fit.py (2832, 2022-04-21)
LICENSE (1067, 2022-04-21)
MasterFC (0, 2022-04-21)
MasterFC\master_get.py (13319, 2022-04-21)
Models (0, 2022-04-21)
Models\BiDAF (0, 2022-04-21)
Models\BiDAF\__pycache__ (0, 2022-04-21)
Models\BiDAF\__pycache__\wrapper.cpython-36.pyc (12129, 2022-04-21)
Models\BiDAF\bidaf_model.py (7676, 2022-04-21)
Models\BiDAF\wrapper.py (14970, 2022-04-21)
Models\FCWithEvidences (0, 2022-04-21)
Models\FCWithEvidences\DeClare (0, 2022-04-21)
Models\FCWithEvidences\DeClare\__pycache__ (0, 2022-04-21)
Models\FCWithEvidences\DeClare\__pycache__\pack.cpython-36.pyc (2802, 2022-04-21)
Models\FCWithEvidences\DeClare\pack.py (2799, 2022-04-21)
Models\FCWithEvidences\__pycache__ (0, 2022-04-21)
Models\FCWithEvidences\__pycache__\basic_fc_model.cpython-36.pyc (4072, 2022-04-21)
Models\FCWithEvidences\__pycache__\graph_based_semantic_structure.cpython-36.pyc (9772, 2022-04-21)
Models\FCWithEvidences\__pycache__\hierachical_multihead_attention.cpython-36.pyc (9857, 2022-04-21)
Models\FCWithEvidences\basic_fc_model.py (5801, 2022-04-21)
Models\FCWithEvidences\graph_based_semantic_structure.py (13853, 2022-04-21)
Models\__pycache__ (0, 2022-04-21)
Models\__pycache__\base_model.cpython-36.pyc (6160, 2022-04-21)
Models\base_model.py (7419, 2022-04-21)
__init__.py (23, 2022-04-21)
formatted_data (0, 2022-04-21)
formatted_data\.DS_Store (6148, 2022-04-21)
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# GET Source code and datasets for the paper "Evidence-aware Fake News Detection with Graph Neural Networks". ## Requirements We use Pytorch 1.9.1 and python 3.6. Other requirements are in requirements.txt. ``` pip install -r requirements.txt ``` ## Data 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 ## Usage You can run the commands below to train and test our model on Snopes Dataset. ``` python MasterFC/master_get.py --dataset="Snopes" \ --cuda=1 \ --fixed_length_left=30 \ --fixed_length_right=100 \ --log="logs/get" \ --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=123756 \ --gsl_rate=0.6 ``` You can also simply run the bash script. ``` sh run_snopes.sh ``` or ``` sh run_politifact.sh (on the PolitiFact dataset) ``` ## Citation Please cite our paper if you use the code: ``` @misc{xu2022evidenceaware, title={Evidence-aware Fake News Detection with Graph Neural Networks}, author={Weizhi Xu and Junfei Wu and Qiang Liu and Shu Wu and Liang Wang}, year={2022}, eprint={2201.06885}, archivePrefix={arXiv}, primaryClass={cs.CL} } ``` ## Acknowledgement The general structure of our codes inherites from the open-source codes of [MAC](https://github.com/nguyenvo09/EACL2021), we thank them for their great contribution to the research community of fake news detection.

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