EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
Yaqing Wang,
Fenglong Ma,
Zhiwei Jin,
Ye Yuan,
Guangxu Xun,
Kishlay Jha,
Lu Su,
Jing Gao
SUNY Buffalo. KDD, 2018.
We recently release a dataset (in Chinese) on fake news from Wechat. The dataset includes news titile, report content, news url and image url. Find more details via https://github.com/yaqingwang/WeFEND-AAAI20
The data folder contains a subset of weibo dataset for a quick start. If you are interested in full weibo dataset, you can download it via https://drive.google.com/file/d/14VQ7EWPiFeGzxp3XC2DeEHi-BEisDINn/view?usp=sharing. (Approximately 1.3GB)
One of the unique challenges for fake news detection on social media is how to identify fake news on newly emerged events. The EANN is desgined to extract shared features among all events to effectively improve the performance of fake news detection on never-seen events.
Comparision between reduced model (w/o adversarial) and EANN(w adversarial)
The feature representations learned by the proposed model EANN (right) are more discriminable than fake news detection (w/o adv).
If this code or dataset is useful for your research, please cite our paper:
@inproceedings{wang2018eann,
title={EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection},
author={Wang, Yaqing and Ma, Fenglong and Jin, Zhiwei and Yuan, Ye and Xun, Guangxu and Jha, Kishlay and Su, Lu and Gao, Jing},
booktitle={Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery \& Data Mining},
pages={849--857},
year={2018},
organization={ACM}
}