Self-Presentation and Information Disclosure on Twitter: Understanding Patterns along Demographic Lines

Nina Cesare, University of Washington
Emma Spiro, University of Washington
Hedwig Lee, University of Washington

Twitter data offers a wealth of opportunity for demographic research. However, one major challenge in this area is that individual-level characteristics of interest (e.g. age, race, gender) are not always made explicitly visible by users. Our goal for this project is to better understand patterns and mechanisms underlying missingness in information disclosure within Twitter user metadata by engaging in a mixed-methods analysis of users’ profiles and behavior. In particular, we are interested in whether or not there are systematic differences in disclosure across demographic characteristics such as age, race and gender within this semi-anonymous site, and why these differences may exist. Our motivation for doing so is to provide a theoretical and methodological foundation for user-centric analyses of Twitter, as well as expand the scope of demographic research questions that can be addressed with this data source.

  See extended abstract

Presented in Session 82: Big Data for Population Research