Multilingual Rumor Detection in Social Media Conversations

This repository contains codes for the paper MUSCAT: Multilingual Rumor Detection in Social Media Conversations, IEEE BigData 2022. The code here implements the MUSCAT model and includes scripts for multiple baselines.

Approach

Data

To conduct experiments using MUSCAT, we used the PHEME, Twitter16, and SEAR datasets. To download the data, you can follow these links:

• PHEME
• Twitter16

Scripts

To conduct experiments using MUSCAT, we used the PHEME, Twitter16, and SEAR datasets. To download the data, you can follow these links:

To run TD-RvNN, execute the following script:

OBJ=PHEME #dataset name
LANG=$en # language split
python torch_model/Main_TD_RvNN.py --obj $OBJ --lang $LANG --fold $i --epochs 300 &

To run BiGCN, execute the following script:

LANG=en
python ./Process/getTwittergraph.py PHEME $LANG
python ./model/Twitter/BiGCN_Twitter.py PHEME 10 $LANG 

To run MUSCAT, execute the following script:

MODEL=bert-base-multilingual-cased
EXP_SETTING=coupled-hierarchical-attn
LANG=en
i=pheme4cls

python run_rumor_opt.py --data_dir ./rumor_data/${i}/${LANG}/${k}/ --train_batch_size 16 \
--task_name ${i} --output_dir ./output_v${OUT_DIR}/${i}_rumor_output_${k}/ --bert_model $MODEL \
--do_train --do_eval --learning_rate 5e-5 --max_tweet_num 17 --max_tweet_length 30 \
--exp_setting $EXP_SETTING # --use_longformer

To cite

@inproceedings{awal2022muscat,
  title={MUSCAT: Multilingual Rumor Detection in Social Media Conversations},
  author={Awal, Md Rabiul and Nguyen, Minh Dang and Lee, Roy Ka-Wei and Choo, Kenny Tsu Wei},
  booktitle={2022 IEEE International Conference on Big Data (Big Data)},
  pages={455--464},
  year={2022},
  organization={IEEE}
}