In this paper, by crawling Facebook public profile pages of a large and diverse user population in New York City, we create a comprehensive and contemporary first name list, in which each name is annotated with a popularity estimate and a gender probability. First, we use the name list as part of a novel and powerful technique for inferring Facebook users’ gender. Our name-centric approach to gender prediction partitions the users into two groups, A and B, and is able to accurately predict genders for users belonging to A. Applying our methodology to NYC users in Facebook, we are able to achieve an accuracy of 95.2% for group A consisting of 95.1% of the NYC users. This is a significant improvement over recent results of gender prediction , which achieved a maximum accuracy of 77.2% based on users’ group affiliations. Second, having inferred the gender of most users in our Facebook dataset, we learn several interesting gender characteristics and analyze how males and females behave in Facebook. We find, for example, that females and males exhibit contrasting behaviors while hiding their attributes, such as gender, age, and sexual preference, and that females are more conscious about their online privacy on Facebook.