This is the implementation of Learning Hierarchical Discourse-level Structure for Fake News Detection paper http://cse.msu.edu/~karimiha/publications/fake_new_discourse2019.pdf
pytorch
sklearn
numpy
pandas
pickle
gensim
- Please download HDSF folder from https://www.dropbox.com/s/shwgf52qlqwoo1n/HDSF.7z?dl=0 (it includes the data, splits, etc)
- Unzip the file and copy it in an arbitrary location, say /PATH/
- Download word embeddings from https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit and copy it to /PATH/HDSF
- To train, run 'python train.py --project_dir /PATH/HDSF/' (hyperparameters are included in config.py)
- To test, 'python test.py --project_dir /PATH/HDSF/'
If you are using this code please cite the following paper
@inproceedings{karimi-tang-2019-learning, title = "Learning Hierarchical Discourse-level Structure for Fake News Detection", author = "Karimi, Hamid and Tang, Jiliang", booktitle = "Proceedings of the 2019 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)", month = jun, year = "2019", address = "Minneapolis, Minnesota", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/N19-1347", doi = "10.18653/v1/N19-1347",pages = "3432--3442"}
To follow my work please follow my webpage http://cse.msu.edu/~karimiha/