Installation | Datasets | User Guide | Benchmark | How to Contribute
This repo includes the Pytorch-Geometric implementation of a series of Graph Neural Network (GNN) based fake news detection models. All GNN models are implemented and evaluated under the User Preference-aware Fake News Detection (UPFD) framework. The fake news detection problem is instantiated as a graph classification task under the UPFD framework.
You can make reproducible run on the virtual machine hosted by the Code Ocean without manual configuration.
We welcome contributions of results of existing models and the SOTA results of new models based on our dataset. You can check the benchmark hosted by PaperWithCode for implemented models and their performances.
If you use the code in your project, please cite the following paper:
SIGIR'21 (PDF)
@inproceedings{dou2021user,
title={User Preference-aware Fake News Detection},
author={Dou, Yingtong and Shu, Kai and Xia, Congying and Yu, Philip S. and Sun, Lichao},
booktitle={Proceedings of the 44nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
year={2021}
}To run the code in this repo, you need to have Python>=3.6, PyTorch>=1.6, and PyTorch-Geometric>=1.6.1.
Please follow the installation instructions of PyTorch-Geometric to install PyG.
Other dependencies can be installed using the following commands:
git clone https://github.com/safe-graph/GNN-FakeNews.git
cd GNN-FakeNews
pip install -r requirements.txtThe dataset can be loaded using the PyG API. You can download the dataset (2.66GB) via the link below and
unzip the data under the \data directory.
https://mega.nz/file/j5ZFEK7Z#KDnX2sjg65cqXsIRi0cVh6cvp7CDJZh1Zlm9-Xt28d4
The dataset includes fake&real news propagation networks on Twitter built according to fact-check information from Politifact and Gossipcop. The news retweet graphs were originally extracted by FakeNewsNet. We crawled near 20 million historical tweets from users who participated in fake news propagation in FakeNewsNet to generate node features in the dataset.
The statistics of the dataset is shown below:
| Data | #Graphs | #Fake News | #Total Nodes | #Total Edges | #Avg. Nodes per Graph |
|---|---|---|---|---|---|
| Politifact | 314 | 157 | 41,054 | 40,740 | 131 |
| Gossipcop | 5464 | 2732 | 314,262 | 308,798 | 58 |
Due to the Twitter policy, we could not release the crawled user historical tweets publicly.
To get the corresponding Twitter user information, you can refer to news lists under \data
and map the news id to FakeNewsNet.
Then, you can crawl the user information by following the instruction on FakeNewsNet.
In the UPFD project, we use Tweepy and Twitter Developer API to get the user information.
We incorporate four node feature types in the dataset, the 768-dimensional bert and 300-dimensional spacy features
are encoded using pretrained BERT and spaCy word2vec, respectively.
The 10-dimensional profile feature is obtained from a Twitter account's profile.
You can refer to profile_feature.py for profile feature extraction.
The 310-dimensional content feature is composed of a 300-dimensional user comment word2vec (spaCy) embedding
plus a 10-dimensional profile feature.
Each graph is a hierarchical tree-structured graph where the root node represents the news, the leaf nodes are Twitter users who retweeted the root news. A user node has an edge to the news node if he/she retweeted the news tweet. Two user nodes have an edge if one user retweeted the news tweet from the other user. The following figure shows the UPFD framework including the dataset construction details You can refer to the paper for more details about the dataset.
All GNN-based fake news detection models are under the \gnn_model directory.
You can fine-tune each model according to arguments specified in the argparser of each model.
The implemented models are as follows:
Since the UPFD framework is built upon the PyG, you can easily try other graph classification models like GIN and HGP-SL under our dataset.
You are welcomed to submit your model, hyper-parameters, and results to this repo via create a pull request. After verifying the results, your model will be added to the repo and the result will be updated to the leaderboard. For other inquiries, please send email to [email protected].
