Resources for misinformation detection on Twitter. This repo is the official resource of the following paper.

• DeMis: Data-efficient Misinformation Detection using Reinforcement Learning, ECML-PKDD 2022.
• [Link][PDF][Slide]

The data sets about COVID-19 misinformation on Twitter presented in our paper are available below.

• COMYTH (weather & home-remedies) - [Datasets]
• COVIDLies - [Paper]

We release our models for misinformation detection on Twitter trained using DeMis method. There are three models trained on three COVID-19 misinformation data sets separately. All trained misinformation detection models are available on my Google Drive 🤗 so you can download models via PyTorch and use it for prediction right away!!!

• DeMis-COMYTH-W (trained on COVID-weather data)
• DeMis-COMYTH-H (trained on COVID-home-remedies data)
• DeMis-COVIDLies (trained on COVIDLies data)

You can use the code to do any of the following purposes: (1) preprocessing tweets, (2) running the model for prediction, or (3) training the model. We tested in pytorch v1.10.1 and transformers v4.18.0.

Specify the input and output filepaths in the shell script run_tweet_preprocessing.sh and run the following command.

sh scripts/run_tweet_preprocessing.sh

Specify the model path, input and output filepaths in the shell script run_detector.sh. Note that you can download the models from the section above and try running it with the following command, or you can train a new detection model using DeMis in the next section.

sh scripts/run_detector.sh

Run a sample script to train using COVID-weather data. See README to learn more on how to train with your data.

sh scripts/run_sample_entire_demis_pipeline.sh

If you feel our paper and resources are useful, please consider citing our work! 🙏

@inproceedings{kawintiranon2022demis,
  title     = {DeMis: Data-efficient Misinformation Detection using Reinforcement Learning},
  author    = {Kawintiranon, Kornraphop and Singh, Lisa},
  booktitle = {Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD)},
  year      = {2022},
  publisher = {Springer}
}

Create an issue here if you have any issues loading models or data sets.