Predicting Which Topics You Will Join in the Future on Social Media

Every day, social media users send millions of microblogs on every imaginable topics. If we could predict which topics a user will join in the future, it would be easy to determine what topics will become popular and what kinds of users a topic may attract. It also can be of great interest for many applications. In this study, we investigate the problem of predicting whether a user will join a topic based on his posting history. We introduce a novel deep convolutional neural network with external neural memory and attention mechanism to perform this problem. User's posting history and topics were modeled with an external neural memory architecture. The convolutional neural network based matching methods were used to construct the relations between users and topics. Final decisions were made based on these matching results. To train and evaluate the proposed method, we collected a large-scale dataset from Twitter. The experimental results demonstrated that the proposed method could perform significantly better than other methods. Comparing to the state-of-the-art deep neural networks, our approach achieves a relative improvement of 18.2\\% in F1-score and 28.9\\% in MAP@10.