SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19

Vera Davydova, Elena Tutubalina


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
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
216–220
Language:
URL:
https://aclanthology.org/2022.smm4h-1.53
DOI:
Bibkey:
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)
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PDF:
https://aclanthology.org/2022.smm4h-1.53.pdf