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 [email protected]

On Twitter-like social media sites, the re-posting statuses or tweets of other users are usually considered to be the key mechanism for spreading information. How to predict whether a tweet will be retweeted by a user has received increasing attention in recent years. Previous methods studied the problem using various linguistic features, personal information of users, and many other manually constructed features to achieve the task. Usually, feature engineering is a laborious task, we require to obtain the external sources and they are difficult or not always available. Recently, deep learning methods have been used in the industry and research community for their ability to learn optimal features automatically and in many tasks, deep learning methods can achieve state-of-the art performance, such as natural language processing, computer vision, image classification and so on. In this work, we proposed a novel attention-based deep neural network to incorporate contextual and social information for this task. We used embeddings to represent the user, the user's attention interests, the author and tweet respectively. To train and evaluate the proposed methods, we also constructed a large dataset collected from Twitter. Experimental results showed that the proposed method could achieve better results than the previous state-of-the-art methods.