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.

• Type: Journal
• Year: 2011
• Where: EMNLP
• Authors: John D. Burger, John C. Henderson, George Kim, Guido Zarrella
• Keywords: Accurate Prediction, Amazon Mechanical Turk, EMNLP 2011, Natural Language Processing, Statistical Models