@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://preview.aclanthology.org/jlcl-multiple-ingestion/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."
}