Improving User Attribute Classification with Text and Social Network Attention

User attribute classification is an important research topic in social media user profiling, which has great commercial value in modern advertisement systems. Existing research on user profiling has mostly focused on manually handcrafted features for different attribute classification tasks. However, these research has partly overlooked the social relation of users. We propose an end-to-end neural network model called the social convolution attention neural network. Our model leverages the convolution attention mechanism to automatically extract user features with respect to different attributes from social texts. The proposed model can capture the social relation of users by combining semantic context and social network information, and improve the performance of attribute classification. We evaluate our model in the gender, age, and geography classification tasks based on the dataset from SMP CUP 2016 competition, respectively. The experimental results demonstrate that the proposed model is effective in automatic user attribute classification with a particular focus on fine-grained user information. We propose an effective model based on the convolution attention mechanism and social relation information for user attribute classification. The model can significantly improve the accuracy in various user attribute classification tasks.