This project is focused on "pronoun-slash-lists." A pronoun-slash-list (PSL) is a list of two or three pronouns separated by slashes, e.g. "he/him." PSLs have become increasingly common in self-descriptive texts. Specifically, we will measure the prevalence of PSLs within Twitter profile bios among likely American users. By prevalence, we mean estimating the quantity: per-10,000 American users of Twitter, how many include a PSL within their profile bio. We will describe the trend in this quantity over time; preliminary work indicates the pronouns he, him, she and her were the fastest growing tokens within user bios from 2015-2020. Second, we will estimate which other tokens were most "predictive" that a user would later add a PSL to their bio. By predictive, we simply mean temporal precedence (i.e. which tokens, normalized by their prevalence generally, are over-abundant in bios that would eventually contain a PSL); we will not make causal claims nor intervene experimentally. Thirdly, we will attempt to measure the clustering of bios containing PSLs within the Twitter social network. That network will be defined by follows, at-mentions, co-mentions or some combination of these indicators of social tie. The sample is constructed from a 1% sample of all Tweets available through the Twitter API and that the authors have been using since 2015. We filter users to likely American users by examining the "location" field within the bio. PSLs are a new social norm that has diffused through digital media (e.g. these bios, email signatures). We have the opportunity to study the spread of this norm in a way that few other phenomena could match.
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