{
 "id": "veranchos/arg_mining_tweets",
 "owner": "veranchos",
 "name": "arg_mining_tweets",
 "downloads": 7,
 "likes": 1,
 "last_modified": "2023-09-27T08:30:45.000Z",
 "stated_licence": "afl-3.0",
 "stated_from": "tag",
 "licence_name": null,
 "licence_link": null,
 "file_licence": null,
 "licence_files": [],
 "sources": [],
 "gated": false,
 "tasks": [],
 "citation": "@inproceedings{davydova-tutubalina-2022-smm4h,\n    title = \"{SMM}4{H} 2022 Task 2: Dataset for stance and premise detection in tweets about health mandates related to {COVID}-19\",\n    author = \"Davydova, Vera  and\n      Tutubalina, Elena\",\n    booktitle = \"Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop {\\&} Shared Task\",\n    month = oct,\n    year = \"2022\",\n    address = \"Gyeongju, Republic of Korea\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2022.smm4h-1.53\",\n    pages = \"216--220\",\n    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.\",\n}",
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 "reason": "The checks could not identify a recognised licence ID in the dataset's metadata.",
 "revision": "d14d45b0ae44f408604e3ec309433ddf19c4260d",
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}