This repo contains the code for cross-lingual misinformation detection. See paper 📔 here.

Install PyTorch 1.1.0 from the official website. Install other dependencies in requirements.txt.

For details of the data, see

• https://gitlab.com/NLP4IF/nlp4if-2021
• https://www.aclweb.org/portal/content/nlp4if-2021-shared-tasks

cd src
python prepare_data.py  # prepare data without using additional data
python prepare_data_additional.py  # prepare data with using additional data

Analysis of the data is available in notebooks/analyze_data.ipynb and notebooks/analyze_data_additional.ipynb.

Choose the appropriate file in the bash folder to train without using additional data or the folder bash_additional to use additional data for training. For example, if you want to fine-tune multilingual BERT with source language English while using the additional data, run the following command lines.

cd bash_additional
chmod +x train_multilingual_bert_src_en.sh
./train_multilingual_bert_src_en.sh

The training logs are saved in the specified file, the argument for which is --log_file_path. The log file also stores the evaluation results after training completes.

Note: To tabulate the results from the log files and pick the best hyperparameters across multiple runs, see notebooks/tabulate_results_v{1,2,3}.ipynb.

cd bash_predict
chmod +x predict_best_sys.sh
./predict_best_sys.sh

• logs_v1 contains the training logs while using own train-dev splits for en and ar and provided train and dev data for bg.
• logs_v2 contains the training logs while using the provided train and dev data for all languages.
• logs contains the training logs while using the provided additional train and dev data for all languages.

@inproceedings{detecting-multilingual-misinformation,
    title = "Detecting Multilingual {COVID}-19 Misinformation on Social Media via Contextualized Embeddings",
    author = "Panda, Subhadarshi and Levitan, Sarah Ita",
    booktitle = "Proceedings of the Fourth Workshop on Natural Language Processing for Internet Freedom: Censorship, Disinformation, and Propaganda",
    series = {NLP4IF@NAACL'~21},
    month = {June},
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
}