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.
Cite
CITATION STYLE
Wang, Z., & Jurgens, D. (2018). It’s going to be okay: Measuring access to support in online communities. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, EMNLP 2018 (pp. 33–45). Association for Computational Linguistics. https://doi.org/10.18653/v1/d18-1004
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