@inproceedings{castro-etal-2018-crowd,
    title = "A Crowd-Annotated {S}panish Corpus for Humor Analysis",
    author = "Castro, Santiago  and
      Chiruzzo, Luis  and
      Ros{\'a}, Aiala  and
      Garat, Diego  and
      Moncecchi, Guillermo",
    editor = "Ku, Lun-Wei  and
      Li, Cheng-Te",
    booktitle = "Proceedings of the Sixth International Workshop on Natural Language Processing for Social Media",
    month = jul,
    year = "2018",
    address = "Melbourne, Australia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/W18-3502",
    doi = "10.18653/v1/W18-3502",
    pages = "7--11",
    abstract = "Computational Humor involves several tasks, such as humor recognition, humor generation, and humor scoring, for which it is useful to have human-curated data. In this work we present a corpus of 27,000 tweets written in Spanish and crowd-annotated by their humor value and funniness score, with about four annotations per tweet, tagged by 1,300 people over the Internet. It is equally divided between tweets coming from humorous and non-humorous accounts. The inter-annotator agreement Krippendorff{'}s alpha value is 0.5710. The dataset is available for general usage and can serve as a basis for humor detection and as a first step to tackle subjectivity.",
}