User demographic attributes

such as gender and age

are the core factors to be considered for research and applications in computational psychology

personalized search and social commerce marketing.Automatic user latent attribute inference based on user generated data becomes an emerging research topic.This paper proposes a methed for user gender inference on Microblog by exploiting user content preferences and following behaviour preferences.The experiments on a dataset collected from Sina Weibo that consists of nearly 10000 users demonstrate the effectiveness of user preferences features.Comparing with the traditional language usage features

combining user content preferences and user following preferences features can improve the inference accuracy largely.The user following preferences features are especially effective for inferring the gender of inactive users.