What is he/she like?: Estimating Twitter user attributes from contents and social neighbors

We propose a new method for estimating user attributes (gender, age, occupation, and interests) of a Twitter user from the user's contents (profile document and tweets) and social neighbors, i.e. those whom the user has mentioned. Our labeling method is able to collect a large amount of training data automatically by using Twitter users associated with a blog account. Furthermore, we experiment estimation methods using social neighbors with three adjustable levels of its information and show that our method, which uses the target user's profile document and tweets and the neighbors' profile documents (not including tweets), achieves the best accuracy.