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CRIPAC-DIG / CF-FEND

[SIGIR 2022] Source code and datasets for "Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention".
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causal-inference causal-intervention debiasing evidence-based fake-news-detection

readme

CF-FEND

model

This is the code for the Paper: Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention.

Usage

Our dataset can be downloaded by this link. Place it in the same directory as counterfactual_inference.

You can run the bash scripts in the directory (run) to train and test models (MAC/bert) on Snopes (sens) or PolitiFact (pomt) Dataset.

For example, run the mac model on Snopes:

sh run/run_mac_snes.sh

Requirements

Required packages are in requirements.txt. You can install packages as follows:

pip install -r requirements.txt

Citation

Please cite our paper if you use the code:

@inproceedings{wu2022bias,
  title     = {Bias Mitigation for Evidence-aware Fake News Detection by Causal Intervention},
  author    = {Wu, Junfei and Liu, Qiang and Xu, Weizhi and Wu, Shu},
  booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  pages     = {2308--2313},
  year      = {2022}
}

Acknowledge

We refer to the work and code of fake news reasoning. We sincerely thank them for their great contribution to the research community of fake news detection.