Faculty Collaboration Database

Mining Twitter as a First Step toward Assessing the Adequacy of Gender Identification Terms on Intake Forms. AMIA Annu Symp Proc 2015;2015:611-20

Date

01/01/2015

Pubmed ID

26958196

Pubmed Central ID

PMC4765681

Scopus ID

2-s2.0-84979630639 (requires institutional sign-in at Scopus site)   14 Citations

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.

Author List

Hicks A, Hogan WR, Rutherford M, Malin B, Xie M, Fellbaum C, Yin Z, Fabbri D, Hanna J, Bian J

Author

William R. Hogan MD Director, Professor in the Data Science Institute department at Medical College of Wisconsin

MESH terms used to index this publication - Major topics in bold

Data Mining
Female
Gender Identity
Humans
Language
Male
Sexual and Gender Minorities
Social Media
Transgender Persons