Relational User Attribute Inference in Social Media

Nowadays, more and more people are engaged in social media to generate multimedia information, i.e., creating text and photo profiles and posting multimedia messages. Such multimodal social networking activities reveal multiple user attributes such as age, gender, and personal interest. Inferring user attributes is important for user profiling, retrieval, and personalization. Existing work is devoted to inferring user attributes independently and ignores the dependency relations between attributes. In this work, we investigate the problem of relational user attribute inference by exploring the relations between user attributes and extracting both lexical and visual features from online user-generated content. We systematically study six types of user attributes: gender, age, relationship, occupation, interest, and emotional orientation. In view of methodology, we propose a relational latent SVM (LSVM) model to combine a rich set of user features, attribute inference, and attribute relations in a unified framework. In the model, one attribute is selected as the target attribute and others are selected as the auxiliary attributes to assist the target attribute inference. The model infers user attributes and attribute relations simultaneously. Extensive experiments conducted on a collected dataset from Google+ with full attribute annotations demonstrate the effectiveness of the proposed approach in user attribute inference and attribute-based user retrieval.