@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",
editor = "Gonzalez-Hernandez, Graciela and
Weissenbacher, Davy",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19
%A Davydova, Vera
%A Tutubalina, Elena
%Y Gonzalez-Hernandez, Graciela
%Y Weissenbacher, Davy
%S Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
%D 2022
%8 October
%I Association for Computational Linguistics
%C Gyeongju, Republic of Korea
%F davydova-tutubalina-2022-smm4h
%X 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.
%U https://aclanthology.org/2022.smm4h-1.53
%P 216-220
Markdown (Informal)
[SMM4H 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to COVID-19](https://aclanthology.org/2022.smm4h-1.53) (Davydova & Tutubalina, SMM4H 2022)
ACL