/M3FEND

Official repository for "Memory-Guided Multi-View Multi-Domain Fake News Detection", IEEE TKDE.

Primary LanguagePython

Memory-Guided Multi-View Multi-Domain Fake News Detection (M3FEND)

This is the official implementation of our paper Memory-Guided Multi-View Multi-Domain Fake News Detection, which has been published in TKDE. Paper

The wide spread of fake news is increasingly threatening both individuals and society. Great efforts have been made for automatic fake news detection on a single domain (e.g., politics). However, correlations exist commonly across multiple news domains, and thus it is promising to simultaneously detect fake news of multiple domains. Based on our analysis, we pose two challenges in multi-domain fake news detection: 1) domain shift, caused by the discrepancy among domains in terms of words, emotions, styles, etc. 2) domain labeling incompleteness, stemming from the real-world categorization that only outputs one single domain label, regardless of topic diversity of a news piece. In this paper, we propose a Memory-guided Multi-view Multi-domain Fake News Detection Framework (M3FEND) to address these two challenges. We model news pieces from a multi-view perspective, including semantics, emotion, and style. Specifically, we propose a Domain Memory Bank to enrich domain information which could discover potential domain labels based on seen news pieces and model domain characteristics. Then, with enriched domain information as input, a Domain Adapter could adaptively aggregate discriminative information from multiple views for news in various domains. Extensive offline experiments on English and Chinese datasets demonstrate the effectiveness of M3FEND, and online tests verify its superiority in practice.

Introduction

This repository provides the implementations of M3FEND and ten baseline models (BiGRU, TextCNN, RoBERTa, StyleLSTM, DualEmotion, EANN, EDDFN, MMoE, MoSE, MDFEND). Note that TextCNN and BiGRU are implemented with word2vec as word embedding in the original experiments, but we implement them with RoBERTa embedding in this repository.

Requirements

  • Python 3.6
  • PyTorch > 1.0
  • Pandas
  • Numpy
  • Tqdm

Run

Parameter Configuration:

  • dataset: the English or Chinese dataset, default for ch
  • early_stop: default for 3
  • domain_num: the Chinese dataset could choose 3, 6, and 9, while the English dataset could choose 3, default for 3
  • epoch: training epoches, default for 50
  • gpu: the index of gpu you will use, default for 0
  • lr: learning_rate, default for 0.0001
  • model_name: model_name within textcnn bigru bert eann eddfn mmoe mose dualemotion stylelstm mdfend m3fend, default for m3fend

You can run this code through:

python main.py --gpu 1 --lr 0.0001 --model_name m3fend --dataset ch --domain_num 3
python main.py --gpu 1 --lr 0.0001 --model_name m3fend --dataset ch --domain_num 6
python main.py --gpu 1 --lr 0.0001 --model_name m3fend --dataset ch --domain_num 9
python main.py --gpu 1 --lr 0.0001 --model_name m3fend --dataset en --domain_num 3

The best learning rate for various models are different: BiGRU (0.0009), TextCNN (0.0007), RoBERTa (7e-05), StyleLSTM(0.0007), DualEmotion(0.0009), EANN (0.0001), EDDFN (0.0007), MDFEND (7e-5), M$^3$FEND (0.0001).

Reference

Zhu, Yongchun, et al. "Memory-Guided Multi-View Multi-Domain Fake News Detection." IEEE Transactions on Knowledge and Data Engineering (2022).

Nan, Qiong, et al. "MDFEND: Multi-domain fake news detection." Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021.

or in bibtex style:

@article{zhu2022memory,
  title={Memory-Guided Multi-View Multi-Domain Fake News Detection},
  author={Zhu, Yongchun and Sheng, Qiang and Cao, Juan and Nan, Qiong and Shu, Kai and Wu, Minghui and Wang, Jindong and Zhuang, Fuzhen},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2022},
  publisher={IEEE}
}
@inproceedings{nan2021mdfend,
  title={MDFEND: Multi-domain fake news detection},
  author={Nan, Qiong and Cao, Juan and Zhu, Yongchun and Wang, Yanyan and Li, Jintao},
  booktitle={Proceedings of the 30th ACM International Conference on Information \& Knowledge Management},
  pages={3343--3347},
  year={2021}
}