This is the implementation of FANG - a graph representation learning framework for fake news detection.
For more details, please refer to our paper. Van-Hoang Nguyen, Kazunari Sugiyama, Preslav Nakov, Min-Yen Kan, FANG: Leveraging Social Context for Fake News Detection Using Graph Representation (CIKM 2020)
conda env create -f environment. yml - conda 4.8.2
- python 3.7.7
- torch 1.5.1
- tensorboard 1.15.0
- GPU: Titan RTX 24220MiB total memory
- CPU: 16GiB total memory
We provided the processed data used in our experiments in data/news_graph
| Description | File name | Format |
|---|---|---|
| Social entities | entities.txt | |
| Social entity's features | entity_features.tsv | entity_id [tab] feature_1_value [tab] feature_2_value... |
| User-news support interactions with negative sentiment | support_negative.tsv | user_id [tab] news_id [tab] seconds_since_publication |
| User-news support interactions with neutral sentiment | support_neutral.tsv | user_id [tab] news_id [tab] seconds_since_publication |
| User-news deny interactions | deny.tsv | user_id [tab] news_id [tab] seconds_since_publication |
| User-news report interactions | report.tsv | user_id [tab] news_id [tab] seconds_since_publication |
| News information | news_info.tsv | news_id [tab] labels [tab] title |
| Indicator whether certain pair of entities should be closed or far, only used for evaluation, not for as labels | rep_entities.tsv | entity_1_id [tab] entity_2_id [tab] closed/far [tab] stance |
| Source-source citation interactions | source_citation.tsv | source_1_id [tab] source_2_id |
| Source-news publication interactions | source_publication.tsv | source_id [tab] news_id |
| User-user friendship interactions | user_relationships.tsv | user_1_id [tab] user_2_id |
| Train-val-test splits (representative of a fold) | train_test_{training percentage}.json | {"train": train_entities, "val": validate_entities, "test": test_entities} |
Unprocessed data, including news and users who engage them can be found in data/fang_fake.csv and data/fang_real.csv.
usage: run_graph.py [-h] [-t TASK] [-m MODEL] [-p PATH] [--percent PERCENT]
[--temporal] [--use-stance] [--use-proximity]
[--epochs EPOCHS] [--attention]
[--pretrained_dir PRETRAINED_DIR]
[--pretrained_step PRETRAINED_STEP]
Graph Learning
optional arguments:
-h, --help show this help message and exit
-t TASK, --task TASK task name
-m MODEL, --model MODEL
model name
-p PATH, --path PATH path to dataset
--percent PERCENT
--temporal whether to use temporality
--use-stance whether to use stance
--use-proximity whether to use proximity
--epochs EPOCHS number of epochs
--attention whether to use attention
--pretrained_dir PRETRAINED_DIR
path to pre-trained model directory
--pretrained_step PRETRAINED_STEP
pre-trained model step
Training FANG for 30 epochs at 90% data with temporality, stance loss and proximity loss.
python run_graph.py -t fang -m graph_sage -p data/news_graph --percent 90 --epochs=30 --attention --use-stance --use-proximity --temporal
Training GCN baseline for 1000 epochs at 90% data.
python run_graph.py -t news_graph -m gcn -p data/news_graph --percent 90 --epochs=1000
- Relation filtering, Stance detection, Sentiment Classification models can be found here
- Social media retriever used to crawl unprocessed data, implemented by Kai Shu et al. can be found here
- The implementation of GraphSage from which this code was adapted can be found here
Please cite our paper as below if you use this code in your work:
@inproceedings{10.1145/3340531.3412046,
author = {Nguyen, Van-Hoang and Sugiyama, Kazunari and Nakov, Preslav and Kan, Min-Yen},
title = {FANG: Leveraging Social Context for Fake News Detection Using Graph Representation},
year = {2020},
isbn = {9781450368599},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3340531.3412046},
doi = {10.1145/3340531.3412046},
abstract = {We propose Factual News Graph (FANG), a novel graphical social context representation and learning framework for fake news detection. Unlike previous contextual models that have targeted performance, our focus is on representation learning. Compared to transductive models, FANG is scalable in training as it does not have to maintain all nodes, and it is efficient at inference time, without the need to re-process the entire graph. Our experimental results show that FANG is better at capturing the social context into a high fidelity representation, compared to recent graphical and non-graphical models. In particular, FANG yields significant improvements for the task of fake news detection, and it is robust in the case of limited training data. We further demonstrate that the representations learned by FANG generalize to related tasks, such as predicting the factuality of reporting of a news medium.},
booktitle = {Proceedings of the 29th ACM International Conference on Information & Knowledge Management},
pages = {1165–1174},
numpages = {10},
keywords = {representation learning, fake news, social networks, graph neural networks, disinformation},
location = {Virtual Event, Ireland},
series = {CIKM '20}
}