This is the official repository of the paper:
Integrating Pattern- and Fact-based Fake News Detection via Model Preference Learning.
Qiang Sheng*, Xueyao Zhang*, Juan Cao, and Lei Zhong.
Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021)
PDF / Poster / Code / Chinese Blog
The experimental datasets where can be seen in dataset folder, including the Weibo Dataset, and the Twitter Dataset. Note that you can download the datasets only after an "Application to Use the Datasets for Pattern- and Fact-based Joint Fake News Detection" has been submitted.
python==3.6.10
torch==1.6.0
torchvision==0.7.0
torch-geometric==1.7.0
torch-sparse==0.6.9
transformers==3.2.0
Notice that Step1 is not necessary, because we have supplied the recognized results in dataset json files in dataset folder.
If you would like to know about the details of the recoginition procedure, you can refer to the preprocess/tokens_recognition folder.
cd preprocess/tokenize
As the run.sh shows, you need to run:
python get_post_tokens.py --dataset [dataset] --pretrained_model [bert_pretrained_model]
cd preprocess/graph_init
As the run.sh shows, you need to run:
python init_graph.py --dataset [dataset] --max_nodes [max_tokens_num]
Notice that Step3 is not necessary if you wouldn't use fact-based models as a componet for Pref-FEND.
cd preprocess/tokenize
As the run.sh shows, you need to run:
python get_articles_tokens.py --dataset [dataset] --pretrained_model [bert_pretrained_model]
cd preprocess/bm25
As the run.sh shows, you need to run:
python retrieve.py --dataset [dataset]
Notice that Step5 is not necessary if you wouldn't use EANN-Text or BERT-Emo as a componet for Pref-FEND.
cd preprocess/EANN_Text
As the run.sh shows, you need to run:
python events_clustering.py --dataset [dataset] --events_num [clusters_num]
cd preprocess/BERT_Emo/code/preprocess
As the run.sh shows, you need to run:
python input_of_emotions.py --dataset [dataset]
cd model
mkdir ckpts
Here we list all of our configurations and running script in run_weibo.sh and run_twitter.sh. For example, if you would like to run BERT-Emo (Pattern-based) + MAC (Fact-based) with Pref-FEND on Weibo, you can run:
# BERT_Emo + MAC (Pref-FNED)
CUDA_VISIBLE_DEVICES=0 python main.py --dataset 'Weibo' \
--use_preference_map True --use_pattern_based_model True --use_fact_based_model True \
--pattern_based_model 'BERT_Emo' --fact_based_model 'MAC' \
--lr 5e-6 --batch_size 4 --epochs 50 \
--save 'ckpts/BERT_Emo+DeClarE_with_Pref-FEND'
then the results will be saved in ckpts/BERT_Emo+DeClarE_with_Pref-FEND.
@inproceedings{Pref-FEND,
author = {Qiang Sheng and
Xueyao Zhang and
Juan Cao and
Lei Zhong},
title = {Integrating Pattern- and Fact-based Fake News Detection via Model
Preference Learning},
booktitle = {{CIKM} '21: The 30th {ACM} International Conference on Information
and Knowledge Management, Virtual Event, Queensland, Australia, November
1 - 5, 2021},
pages = {1640--1650},
year = {2021},
url = {https://doi.org/10.1145/3459637.3482440},
doi = {10.1145/3459637.3482440}
}
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