This is the PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection (Arxiv).

@article{jin2021towards,
  title={Towards Fine-Grained Reasoning for Fake News Detection},
  author={Jin, Yiqiao and Wang, Xiting and Yang, Ruichao and Sun, Yizhou and Wang, Wei and Liao, Hao and Xie, Xing},
  journal={arXiv preprint arXiv:2110.15064},
  year={2021}
}

The implementation is based on HuggingFace Transformers and KernelGAT.

• Run conda install pytorch==1.7.1 torchvision==0.8.2 torchaudio==0.7.2 cudatoolkit=10.2 -c pytorch. conda is preferred over pip due to its stability on Windows

• Take politifact as an example. Make sure you have put the following training and test files under data/.
  • Train_bert-base-cased_politifact_130_5.pt
  • Test_bert-base-cased_politifact_130_5.pt
• If the Train_*.pt and Test_*.pt files are not present, you can run preprocess/preprocess.py to split the training data (e.g. bert-base-cased_politifact_130_5.pt) into Train_*.pt and Test_*.pt. You can download the data here
• Download the files for pretrained BERT model and put them under bert_base/. You should have the following 3 files in bert_base/:
  • pytorch_model.bin
  • vocab.txt
  • bert_config.json
• make sure you have set the root path given by get_root_dir() in utils/utils to your own data path of fake_news_data/. Mine is root = "C:\\Workspace\\FakeNews\\fake_news_data" on Windows and root = "../../fake_news_data"
• run the train.py file using kgat/ as the working directory:
  • python train.py --outdir . --config_file P.ini, or
  • python train.py --outdir . --config_file G.ini