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.- Anthology ID:
- 2022.smm4h-1.53
- Original:
- 2022.smm4h-1.53v1
- Version 2:
- 2022.smm4h-1.53v2
- Volume:
- Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
- Month:
- October
- Year:
- 2022
- Address:
- Gyeongju, Republic of Korea
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 216–220
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.53
- DOI:
- Cite (ACL):
- Vera Davydova and Elena Tutubalina. 2022. SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 216–220, Gyeongju, Republic of Korea. Association for Computational Linguistics.
- Cite (Informal):
- SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19 (Davydova & Tutubalina, SMM4H 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/2022.smm4h-1.53.pdf