The silent majority speaks: Inferring silent users' opinions in online social networks

With the blossoming of social networking platforms like Twitter and Facebook, how to infer the opinions of online social network users on specific topics they had not directly given yet, has received much attention. Existing solutions mainly rely on one's previous posted messages. However, recent studies show that over 40% of users opt to be silent all or most of the time and post very few messages. Consequently, the performance of existing solutions will drop dramatically when they are applied to infer silent users' opinions, and how to infer the opinions of these silent users becomes a meaningful while challenging task. Inspired by the collaborative filtering techniques in cold-start recommendations, we infer the opinions of silent users by leveraging the text content posted by active users and their relationships between silent users. Specifically, we first consider both observed and pseudo relationships among users, and cluster users into communities in order to extract various kinds of features for opinion inference. We then design a coupled sparse matrix factorization (CSMF) model to capture the complex relations among these features. Extensive experiments on real-world data from Twitter show that our CSMF model achieves over 80% accuracy for the inference of silent users' opinions.