VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter

<p>We create a publicly available dataset of over 3,100 COVID-19 vaccine-related tweets labeled as one of four stance categories: <em>pro-vaxx, anti-vaxx</em>, <em>vaxx-hesitant</em>,<em> or irrelevant</em>.</p> <p><strong>***</strong></p> <p><strong>Please use the V2 version.</strong></p> <p><strong>***</strong></p> <p>We split our dataset into two separate files:</p> <p>(1) VaccineHesitancy_train_v2.csv (Single + Double annotated)</p> <p>(2) VaccineHesitancy_test.csv (Double annotated)</p> <p>We present the details of this dataset here:</p> <p>VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter (ICWSM 2023)</p> <p><strong>Our Pre-trained model</strong> (GateNLP/covid-vaccine-twitter-bert) : https://huggingface.co/GateNLP/covid-vaccine-twitter-bert</p> <p><strong>Paper</strong>: https://ojs.aaai.org/index.php/ICWSM/article/view/22213/21992</p> <p>&nbsp;</p> <pre>@inproceedings{mu2023vaxxhesitancy, title={VaxxHesitancy: A Dataset for Studying Hesitancy Towards COVID-19 Vaccination on Twitter}, author={Mu, Yida and Jin, Mali and Grimshaw, Charlie and Scarton, Carolina and Bontcheva, Kalina and Song, Xingyi}, booktitle={Proceedings of the International AAAI Conference on Web and Social Media}, volume={17}, pages={1052--1062}, year={2023} } </pre> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>