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.