This repository is the official implementation of [Event-Based Time-Aware Rumor Detection Benchmark].
To install requirements:
conda env create -f environment.yaml
To train the model(s) in the paper, run this command:
BERT
cd Improving-Rumor-Detection-with-User-Comments-main
cd PHEME-RNR
python Rumor_BERT_domain_discriminator_data.py train event
cd TWITTER-RNR
python Rumor_BERT_domain_discriminator_data.py train event Twitter15
EANN
cd EANN-KDD18-master
cd src
python EANN_text_data.py train event
python EADD_text_twitter_data.py train event Twitter15
GACL
cd GACL-CADA
cd PHEME_two_classification
python main_pheme_domain_discriminator_data.py train b
cd Twitter_four_classification
python main_twitter_domain_discriminator_data.py train b Twitter15
MetaDetector
cd metadetector-master
cd src
python MetaDetector_data.py train event
python MetaDetector_Twitter_data.py train event Twitter15
Our pretrained models here: BERT Download from 'https://www.aliyundrive.com/s/S9RLpi5fERM' and place 'pheme' in BERT into Improving-Rumor-Detection-with-User-Comments-main/PHEME-RNR/model_all_domain Download from 'https://www.aliyundrive.com/s/S9RLpi5fERM' and place 'twitter' in BERT into Improving-Rumor-Detection-with-User-Comments-main/Twitter-RNR/model_all_domain
EANN Download from 'https://www.aliyundrive.com/s/S9RLpi5fERM' and place the files in EANN into EANN-KDD18-master/data/output/ EANN-KDD18-master/data/output/
GACL GACL-CADA/PHEME_two_classification/model_all_domain GACL-CADA/Twitter_four_classification/model_all_domain
MetaDetector metadetector-master/data/output/
Our model achieves the following performance :
PHEME and PHEME-structure
Twitter15 and Twitter16
Twitter15-structure and Twitter16-structure
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