@inproceedings{hansen-hershcovich-2022-dataset,
title = "A Dataset of Sustainable Diet Arguments on {T}witter",
author = "Hansen, Marcus and
Hershcovich, Daniel",
editor = "Biester, Laura and
Demszky, Dorottya and
Jin, Zhijing and
Sachan, Mrinmaya and
Tetreault, Joel and
Wilson, Steven and
Xiao, Lu and
Zhao, Jieyu",
booktitle = "Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates (Hybrid)",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.nlp4pi-1.5",
doi = "10.18653/v1/2022.nlp4pi-1.5",
pages = "40--58",
abstract = "Sustainable development requires a significant change in our dietary habits. Argument mining can help achieve this goal by both affecting and helping understand people{'}s behavior. We design an annotation scheme for argument mining from online discourse around sustainable diets, including novel evidence types specific to this domain. Using Twitter as a source, we crowdsource a dataset of 597 tweets annotated in relation to 5 topics. We benchmark a variety of NLP models on this dataset, demonstrating strong performance in some sub-tasks, while highlighting remaining challenges.",
}
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<abstract>Sustainable development requires a significant change in our dietary habits. Argument mining can help achieve this goal by both affecting and helping understand people’s behavior. We design an annotation scheme for argument mining from online discourse around sustainable diets, including novel evidence types specific to this domain. Using Twitter as a source, we crowdsource a dataset of 597 tweets annotated in relation to 5 topics. We benchmark a variety of NLP models on this dataset, demonstrating strong performance in some sub-tasks, while highlighting remaining challenges.</abstract>
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%0 Conference Proceedings
%T A Dataset of Sustainable Diet Arguments on Twitter
%A Hansen, Marcus
%A Hershcovich, Daniel
%Y Biester, Laura
%Y Demszky, Dorottya
%Y Jin, Zhijing
%Y Sachan, Mrinmaya
%Y Tetreault, Joel
%Y Wilson, Steven
%Y Xiao, Lu
%Y Zhao, Jieyu
%S Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI)
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates (Hybrid)
%F hansen-hershcovich-2022-dataset
%X Sustainable development requires a significant change in our dietary habits. Argument mining can help achieve this goal by both affecting and helping understand people’s behavior. We design an annotation scheme for argument mining from online discourse around sustainable diets, including novel evidence types specific to this domain. Using Twitter as a source, we crowdsource a dataset of 597 tweets annotated in relation to 5 topics. We benchmark a variety of NLP models on this dataset, demonstrating strong performance in some sub-tasks, while highlighting remaining challenges.
%R 10.18653/v1/2022.nlp4pi-1.5
%U https://aclanthology.org/2022.nlp4pi-1.5
%U https://doi.org/10.18653/v1/2022.nlp4pi-1.5
%P 40-58
Markdown (Informal)
[A Dataset of Sustainable Diet Arguments on Twitter](https://aclanthology.org/2022.nlp4pi-1.5) (Hansen & Hershcovich, NLP4PI 2022)
ACL
- Marcus Hansen and Daniel Hershcovich. 2022. A Dataset of Sustainable Diet Arguments on Twitter. In Proceedings of the Second Workshop on NLP for Positive Impact (NLP4PI), pages 40–58, Abu Dhabi, United Arab Emirates (Hybrid). Association for Computational Linguistics.