title: Mining Twitter as a First Step toward Assessing the Adequacy of Gender Identification Terms on Intake Forms.
abstract: The Institute of Medicine (IOM) recommends that health care providers collect data on gender identity. If these data are to be useful, they should utilize terms that characterize gender identity in a manner that is 1) sensitive to transgender and gender non-binary individuals (trans* people) and 2) semantically structured to render associated data meaningful to the health care professionals. 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 regions and population groups. We offer sample hypotheses regarding regional variations in the usage of certain terms such as 'genderqueer', 'genderfluid', and 'neutrois' and their usefulness as terms on intake forms. While these hypotheses cannot be directly validated with Twitter data alone, our data and tools help to formulate testable hypotheses and design future studies regarding the adequacy of gender identification terms on intake forms.
authors.affiliation: University of Florida, Gainesville, FL.
authors.forename: Amanda
authors.initials: A
authors.lastname: Hicks
authors.affiliation: University of Florida, Gainesville, FL.
authors.forename: William R
authors.initials: WR
authors.lastname: Hogan
authors.affiliation: University of Arkansas for Medical Sciences, Little Rock, AR.
authors.forename: Michael
authors.initials: M
authors.lastname: Rutherford
authors.affiliation: Vanderbilt University, Nashville, TN.
authors.forename: Bradley
authors.initials: B
authors.lastname: Malin
authors.affiliation: University of Arkansas at Little Rock, Little Rock, AR.
authors.forename: Mengjun
authors.initials: M
authors.lastname: Xie
authors.affiliation: Princeton University, Princeton, NJ.
authors.forename: Christiane
authors.initials: C
authors.lastname: Fellbaum
authors.affiliation: Vanderbilt University, Nashville, TN.
authors.forename: Zhijun
authors.initials: Z
authors.lastname: Yin
authors.affiliation: Vanderbilt University, Nashville, TN.
authors.forename: Daniel
authors.initials: D
authors.lastname: Fabbri
authors.affiliation: University of Florida, Gainesville, FL.
authors.forename: Josh
authors.initials: J
authors.lastname: Hanna
authors.affiliation: University of Florida, Gainesville, FL.
authors.forename: Jiang
authors.initials: J
authors.lastname: Bian
mesh_terms: D057225:Data Mining / Q000379:methods*; D005260:Female; D005783:Gender Identity; D006801:Humans; D007802:Language; D008297:Male; D000072339:Sexual and Gender Minorities; D061108:Social Media; D063106:Transgender Persons