Microblog User Profiling: A Supervised Clustering based Approach for Age and Gender Classification User profiling is the process of tagging user attributes, such as gender, age, location and so on. Currently the popular way is to do classification by treating attributes as labels. In addition, ensemble classifications are used to future improve classification quality. However, the base classifier of traditional ensemble classification is trained by using random sampling, which cannot guarantee a constantly good classification performance. This paper proposes a supervised clustering of ensemble classification approach to tag microblog users with age and gender attributes. The proposed approach is to divide the training data with a same label into multiple clusters and then combine different clusters from training data with different labels into training data subsets. Each subset is used to train a base classifier. Experimental results show that the proposed approach can improve the gender and age classification accuracy classification by 1.79 % and 0.67 % respectively, compared with the traditional ensemble classification approach.

Mining Crossplatform User Behaviors for Demographic Attribute Inference In this paper we seek to utilize the behavior information and the attribute labels of users from an auxiliary platform to help make attribute prediction. We propose a crossplatform demographic attribute inference model (CDAIM for short), in which we first learn the user representations with the behavior data, and then regulate the vectors of the same users from both platform to be close via a transfer function, and finally train a classifier with the feature vectors and attribute labels of all the users. We conduct extensive experiments on real datasets and the results show that our CDAIM outperforms the baselines.