Discriminating Gender on Twitter

9 years 11 months ago
Discriminating Gender on Twitter
Accurate prediction of demographic attributes from social media and other informal online content is valuable for marketing, personalization, and legal investigation. This paper describes the construction of a large, multilingual dataset labeled with gender, and investigates statistical models for determining the gender of uncharacterized Twitter users. We explore several different classifier types on this dataset. We show the degree to which classifier accuracy varies based on tweet volumes as well as when various kinds of profile metadata are included in the models. We also perform a large-scale human assessment using Amazon Mechanical Turk. Our methods significantly out-perform both baseline models and almost all humans on the same task.
John D. Burger, John C. Henderson, George Kim, Gui
Added 20 Dec 2011
Updated 20 Dec 2011
Type Journal
Year 2011
Authors John D. Burger, John C. Henderson, George Kim, Guido Zarrella
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