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EANN: event-adversarial neural networks for multi-modal fake news detection
| Date | Stars |
|---|---|
| 2026-07-31 | 280 |
| 2026-08-04 | 280 |
| 2026-08-05 | 280 |
| 2026-08-06 | 280 |
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# EANN-KDD18
[EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection](https://dl.acm.org/citation.cfm?id=3219819.3219903)
[Yaqing Wang](http://www.acsu.buffalo.edu/~yaqingwa/),
[Fenglong Ma](http://personal.psu.edu/ffm5105/),
[Zhiwei Jin](https://scholar.google.com/citations?user=iv22mK4AAAAJ&hl=zh-CN),
[Ye Yuan](https://scholar.google.com/citations?user=97ZPgN4AAAAJ&hl=en&authuser=1),
[Guangxu Xun](https://scholar.google.com/citations?user=HhyfdQYAAAAJ&hl=en),
[Kishlay Jha](http://people.virginia.edu/~kj6ww/),
[Lu Su](https://cse.buffalo.edu/~lusu/),
[Jing Gao](https://cse.buffalo.edu/~jing/)
SUNY Buffalo. KDD, 2018.
## Dataset
**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)
## Main Idea
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.
## Experiment
Comparision between reduced model (w/o adversarial) and EANN(w adversarial)
<img src="https://github.com/yaqingwang/EANN-KDD18/blob/master/Fig/Accuracy.png" width="300"> <img src="https://github.com/yaqingwang/EANN-KDD18/blob/master/Fig/F1.png" width="300">
The feature representations learned by the proposed model EANN (right) are more discriminable than fake news detection (w/o adv).
<img src="https://github.com/yaqingwang/EANN-KDD18/blob/master/Fig/baseline_tsne.png" width="256"> <img src="https://github.com/yaqingwang/EANN-KDD18/blob/master/Fig/model_tsne.png" width="256">
## Citation
If this code or dataset is useful for your research, please cite our [paper](https://dl.acm.org/citation.cfm?id=3219819.3219903):
```
@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}
}
```
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