**A Social Media Natural Language Processing package for PyTorch & PyTorch Lightning.** ______________________________________________________________________

Key Features • About Me • How To Use • Examples

[](https://pypi.org/project/pytorch-gleam/) [](https://badge.fury.io/py/pytorch-gleam) [](https://github.com/Supermaxman/pytorch-gleam/blob/master/LICENSE.txt)

PyTorch Gleam

PyTorch Gleam builds upon PyTorch Lightning for the specific use-case of Natural Language Processing on Social Media, such as Twitter. PyTorch Gleam strives to make Social Media NLP research easier to understand, use, and extend. Gleam contains models I use in my research, from fine-tuning a BERT-based model with Lexical, Emotion, and Semantic information in a Graph Attention Network for stance identification towards COVID-19 misinformation, to using Information Retrieval systems to identify new types of misinformation on Twitter.

About Me

My name is Maxwell Weinzierl, and I am a Natural Language Processing researcher at the Human Technology Research Institute (HLTRI) at the University of Texas at Dallas. I am currently working on my PhD, which focuses on COVID-19 and HPV vaccine misinformation, trust, and more on Social Media platforms such as Twitter. I have built PyTorch Gleam to enable easy reproducibility for my published research, and for my own quick iterations on research ideas.

How To Use

Step 0: Install

Simple installation from PyPI

bash pip install pytorch-gleam

You may need to install CUDA drivers and other versions of PyTorch. See PyTorch and PyTorch Lightning for installation help.

Step 1: Create Experiment

Create a configs folder with a YAML experiment file. Gleam utilizes PyTorch Lightning's CLI tools to configure experiments from YAML files, which enables researchers to clearly look back and identify both hyper-parameters and model code used in their experiments. This example is from COVID-19 vaccine misinformation stance identification:

pg_examples/covid-stance.yaml

yaml seed_everything: 0 model: class_path: pytorch_gleam.modeling.models.MultiClassFrameLanguageModel init_args: learning_rate: 5e-4 pre_model_name: digitalepidemiologylab/covid-twitter-bert-v2 label_map: No Stance: 0 Accept: 1 Reject: 2 threshold: class_path: pytorch_gleam.modeling.thresholds.MultiClassThresholdModule metric: class_path: pytorch_gleam.modeling.metrics.F1PRMultiClassMetric init_args: mode: macro num_classes: 3 trainer: max_epochs: 10 accumulate_grad_batches: 4 check_val_every_n_epoch: 1 deterministic: true num_sanity_val_steps: 1 checkpoint_callback: false callbacks: - class_path: pytorch_gleam.callbacks.FitCheckpointCallback data: class_path: pytorch_gleam.data.datasets.MultiClassFrameDataModule init_args: batch_size: 8 max_seq_len: 128 label_name: misinfo label_map: No Stance: 0 Accept: 1 Reject: 2 tokenizer_name: digitalepidemiologylab/covid-twitter-bert-v2 num_workers: 8 frame_path: - covid19/misinfo.json train_path: - covid19/stance-train.jsonl val_path: - covid19/stance-dev.jsonl test_path: - covid19/stance-test.jsonl

Documentation on available models, datasets, and callbacks will be provided soon.

Details about how to set up YAML experiment files are provided by PyTorch Lightning's documentation.

Annotations for this example are provided in the VaccineLies repository under covid19 as the CoVaxLies collection: CoVaxLies. You will need to download the tweet texts from the tweet ids from the Twitter API.

Step 2: Run Experiment

Create a models folder for your saved TensorBoard logs and model weights. Determine the GPU ID for the GPU you would like to utilize (multi-gpu supported) and provide the ID in a list, with a comma at the end if it is a single GPU ID. You can also just specify an integer, such as 1, and PyTorch Lightning will try to find a single free GPU automatically. Run the following command to start training:

bash gleam fit \ --config configs/covid-stance.yaml \ --trainer.gpus 1 \ --trainer.default_root_dir models/covid-stance

Your model will train, with TensorBoard logging all metrics, and a checkpoint will be saved upon completion.

Step 3: Evaluate Experiment

You can easily evaluate your system on a test collection as follows:

bash gleam test \ --config configs/covid-stance.yaml \ --trainer.gpus 1 \ --trainer.default_root_dir models/covid-stance

Examples

These are a work-in-progress, as my original research code is a bit messy, but they will be updated soon!

COVID-19 Vaccine Misinformation Detection on Twitter

@article{weinzierl-covid-glp, title = {Automatic detection of COVID-19 vaccine misinformation with graph link prediction}, author = {Maxwell A. Weinzierl and Sanda M. Harabagiu}, year = 2021, journal = {Journal of Biomedical Informatics}, volume = 124, pages = 103955, doi = {https://doi.org/10.1016/j.jbi.2021.103955}, issn = {1532-0464}, url = {https://www.sciencedirect.com/science/article/pii/S1532046421002847}, keywords = {Natural Language Processing, Machine learning, COVID-19, vaccine misinformation, Social Media, knowledge graph embedding} }

• CoVaxLies V1 with Graph-Link Prediction

COVID-19 Vaccine Misinformation Stance Identification on Twitter

@inproceedings{weinzierl-covid19-acs-stance, author = {Weinzierl, Maxwell and Harabagiu, Sanda}, title = {Identifying the Adoption or Rejection of Misinformation Targeting COVID-19 Vaccines in Twitter Discourse}, year = {2022}, isbn = {9781450390965}, publisher = {Association for Computing Machinery}, address = {New York, NY, USA}, url = {https://doi.org/10.1145/3485447.3512039}, doi = {10.1145/3485447.3512039}, booktitle = {Proceedings of the Web Conference 2022 Forthcoming}, pages = {}, numpages = {10}, location = {Lyon, France}, series = {WWW '22} }

• CoVaxLies V2 with Attitude Consistency Scoring

COVID-19 Misinformation Stance Identification on Twitter

@article{Weinzierl_Hopfer_Harabagiu_2021, title = {Misinformation Adoption or Rejection in the Era of COVID-19}, author = {Weinzierl, Maxwell and Hopfer, Suellen and Harabagiu, Sanda M.}, year = 2021, month = {May}, journal = {Proceedings of the International AAAI Conference on Web and Social Media}, volume = 15, number = 1, pages = {787--795}, url = {https://ojs.aaai.org/index.php/ICWSM/article/view/18103} }

• COVIDLies with Lexical, Emotion, and Semantic GATs for Stance Identification

Vaccine Misinformation Transfer Learning

@misc{weinzierl2022vaccinelies, title = {VaccineLies: A Natural Language Resource for Learning to Recognize Misinformation about the COVID-19 and HPV Vaccines}, author = {Maxwell Weinzierl and Sanda Harabagiu}, year = 2022, eprint = {2202.09449}, archiveprefix = {arXiv}, primaryclass = {cs.CL} }

• COVID-19 to HPV on VaccineLies
• HPV to COVID-19 on VaccineLies

Vaccine Hesitancy Profiling on Twitter

@article{weinzierl-hesitancy-profiling, author = {Weinzierl, Maxwell A. and Hopfer, Suellen and Harabagiu, Sanda M.}, title = {Scaling Up the Discovery of Hesitancy Profiles by Identifying the Framing of Beliefs towards Vaccine Confidence in Twitter Discourse}, elocation-id = {2021.10.01.21264439}, year = {2021}, doi = {10.1101/2021.10.01.21264439}, publisher = {Cold Spring Harbor Laboratory Press}, URL = {https://www.medrxiv.org/content/early/2021/10/06/2021.10.01.21264439}, journal = {medRxiv} }

@misc{weinzierl2022hesitancy, title = {From Hesitancy Framings to Vaccine Hesitancy Profiles: A Journey of Stance, Ontological Commitments and Moral Foundations}, author = {Maxwell Weinzierl and Sanda Harabagiu}, year = 2022, eprint = {2202.09456}, archiveprefix = {arXiv}, primaryclass = {cs.CL} }

• TODO