Gender estimation for SNS user profiling using automatic image annotation

User profiling for Social Network Services (SNS) has gained great attention because of its potential values in identifying target population, which is very informative for marketing. Many studies have been conducted to estimate SNS user profiles using text analysis. However, in spite of the huge quantities of image resources on SNS, no previous work has specifically explored user profiles by automatic image annotation techniques. This paper addresses the problem of inferring a SNS user's gender by automatic image annotation. The proposed method involves learning a model to annotate SNS images and integrating annotation scores of images to infer a user's gender. Evaluation based on Twitter data demonstrates promising results.