@inproceedings{wang-jurgens-2018-going,
    title = "It`s going to be okay: Measuring Access to Support in Online Communities",
    author = "Wang, Zijian  and
      Jurgens, David",
    editor = "Riloff, Ellen  and
      Chiang, David  and
      Hockenmaier, Julia  and
      Tsujii, Jun{'}ichi",
    booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
    month = oct # "-" # nov,
    year = "2018",
    address = "Brussels, Belgium",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/D18-1004/",
    doi = "10.18653/v1/D18-1004",
    pages = "33--45",
    abstract = "People use online platforms to seek out support for their informational and emotional needs. Here, we ask what effect does revealing one`s gender have on receiving support. To answer this, we create (i) a new dataset and method for identifying supportive replies and (ii) new methods for inferring gender from text and name. We apply these methods to create a new massive corpus of 102M online interactions with gender-labeled users, each rated by degree of supportiveness. Our analysis shows wide-spread and consistent disparity in support: identifying as a woman is associated with higher rates of support - but also higher rates of disparagement."
}