RtGender: A Corpus for Studying Differential Responses to Gender

Abstract

Like many social variables, gender pervasively influences how people communicate with one another. However, prior computational work has largely focused on linguistic gender difference and communications about gender, rather than communications directed to people of that gender, in part due to lack of data. Here, we fill a critical need by introducing a multi-genre corpus of more than 25M comments from five socially and topically diverse sources tagged for the gender of the addressee. Using these data, we describe pilot studies on how differential responses to gender can be measured and analyzed and present 30k annotations for the sentiment and relevance of these responses, showing that across our datasets responses to women are more likely to be emotive and about the speaker as an individual (rather than about the content being responded to). Our dataset enables studying socially important questions like gender bias, and has potential uses for downstream applications such as dialogue systems, gender detection or obfuscation, and debiasing language generation.

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