--- license: afl-3.0 --- # Argument mining from Tweets related to COVID-19. This repository contains a dataset for SMM4H'22 Task 2: Classification of stance and premise in tweets about health mandates (COVID-19). Data includes: - [Train](train) and [test](data/test/smm4h) data for SMM4H 2022 Task 2: tweets annotated for stance and premise prediction on three claims about COVID-19 mandates such as stay-at-home-orders, school closures, and face masks - [2070](test/vaccine_tweets) annotated tweets about vaccine mandates, that were not used in the official SMM4H competition - [600](test/vaccine_tweets/unused) annotated tweets about vaccine mandates with low inter-annotators agreement. ## Citation If you find this dataset useful, please cite: ``` @inproceedings{davydova-tutubalina-2022-smm4h, title = "{SMM}4{H} 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to {COVID}-19", author = "Davydova, Vera and Tutubalina, Elena", booktitle = "Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\&} Shared Task", month = oct, year = "2022", address = "Gyeongju, Republic of Korea", publisher = "Association for Computational Linguistics", url = "https://aclanthology.org/2022.smm4h-1.53", pages = "216--220", abstract = "This paper is an organizers{'} report of the competition on argument mining systems dealing with English tweets about COVID-19 health mandates. This competition was held within the framework of the SMM4H 2022 shared tasks. During the competition, the participants were offered two subtasks: stance detection and premise classification. We present a manually annotated corpus containing 6,156 short posts from Twitter on three topics related to the COVID-19 pandemic: school closures, stay-at-home orders, and wearing masks. We hope the prepared dataset will support further research on argument mining in the health field.", } ``` smm4h_graphical_abstract