It's going to be okay: Measuring Access to Support in Online Communities
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.
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