An Analysis of W ord N et’s Coverage of Gender Identity Using T witter and The National Transgender Discrimination Survey (2016)

Abstract: While gender identities in the Western world are typically regarded as binary, our previous work (Hicks et al., 2015) shows that there is more lexical variety of gender identity and the way people identify their gender. There is also a growing need to lexically represent this variety of gender identities. In our previous work, we developed a set of tools and approaches for analyzing Twitter data as a basis for generating hypotheses on language used to identify gender and discuss gender-related issues across geographic regions and population groups in the U.S.A. In this paper we analyze the coverage and relative frequency of the word forms in our Twitter analysis with respect to the National Transgender Discrimination Survey data set, one of the most comprehensive data sets on transgender, gender non-conforming, and gender variant people in the U.S.A. We then analyze the coverage of WordNet, a widely used lexical database, with respect to these identities and discuss some key considerations and next steps for adding gender identity words and their meanings to WordNet.

## introduction gender identity is richly lexicalized in american english. nevertheless, a cursory investigation of gender identity in wordnet @xcite suggests that coverage of non-binary gender identity is low. the goal of our research is to measure the coverage of wordnet's gender identity and to suggest steps to improve it. there is increasing incentive to include gender identity terms and other words that are relevant to transgender, gender variant, non-binary, and gender non-conforming people in wordnet. for example, the institute of medicine (iom) recently recommended (1) gathering data on sexual orientation and gender identity in electronic health records (ehr) as part of the meaningful use objectives in ehrs, (2) developing standardization of sexual orientation and gender identity measures to facilitate synthesizing scientific knowledge about the health of sexual and gender minorities, and (3) supporting research to develop innovative methods of conducting research with small populations to determine the best ways to collect information on lgbt minorities. furthermore, it is important for the medical community to use words that are common among patients and research participants since the use of language that is familiar to the participant has been shown to improve response rates in data collection @xcite @xcite . however, there are challenges to determining which words to include in wordnet and how to define them. based on the limited research available, some evidence @xcite @xcite suggests that vocabulary for self-identifying gender and sexual orientation varies by community. there is clear evidence of lexical variation associated with geography in linguistics studies @xcite @xcite . also, through discussions with members of the trans community and health care providers at lgbt clinics across the country, we have learned that new words are frequently coined to describe gender identity and that the connotations of existing words may vary across communities. we use 'trans' broadly to refer to transgender, transsexual, gender nonconforming, gender variant, and non-binary individuals. user generated content on social media, such as twitter, is a valuable resource because it can pro-vide a source for gleaning information about people's daily lives to answer scientific questions. in our previous work, we produced a data set to investigate words used to discuss gender in the general population and among self-identifying trans persons using twitter @xcite . with 'self-identifying' we refer to people who have stated that they have a trans identity either through their tweets or in the national transgender discrimination survey (ntds) @xcite . we believe that we can augment our twitter data set with the ntds data to produce a data set that is in sync with current speakers' language, that can serve as a starting point for enriching word-net's coverage of gender identity, and that can contribute to the medical and clinical goals outlined at the beginning of this section. the national transgender discrimination survey (ntds) is the largest survey of the trans population in the united states to date @xcite . the survey was designed to collect information about "the broadest possible swath of experiences of transgender and gender nonconforming people" in the u.s.a., including questions about how participants identify their own gender and an option to write in one's own identity @xcite . we have compiled a list of the gender-identity word forms (henceforth simply 'words') from this survey and performed a normalized frequency analysis that can be compared to our twitter data set. in our previous work we built a data set and visualization tools that show relative frequency and co-occurrence networks for american english trans words on twitter @xcite . our goal in this paper is to perform a two-fold coverage analysis of wordnet with respect to american english gender identity. our hypothesis is that a comprehensive list of words used to self-identify gender will require examining the words trans people use in different contexts. in order to evaluate this hypothesis, we perform a frequency analysis of words from both sets. our approach is as follows. first, we compare the trans identity words that we identified in our previous work with the words from the ntds to assess the coverage of the twitter set. next, we produce an updated set of words using the ntds and compare wordnet's coverage of gender identity against this list. ## methods here we describe our language analysis of the twitter data and the ntds data. ## results first we discuss the results of analysis of our twitter data. then we discuss our analysis of word-net's coverage of trans related terms.