Cross media topic analytics based on synergetic content and user behavior modeling

Hot topic in cross media, defined as a set of Web documents containing similar semantic information, describes the same real world event with significant social impact. However, cross media topic detection still remains a challenging issue since it is unclear how users interact with the cross media topics, leading to difficulties in identifying the influence of different topics on different user communities. In this paper, we propose a solution framework for cross media topic analysis based on synergetic modeling of multi-modal content and user behavior. First, we detect atom topics by multi-modal topic detection. Second, we propose a multi-resolution user behavior modeling method to discover communities on the active Web users by considering the distribution of related atom topics along the temporal axis. We analyze the topic-topic, topic-community and community-community relations by using the proposed method. Consequently, the macro-topic and macro-community structures can be obtained for better understanding of the interaction between topics and communities. Experiments show the capability of our method in knowledge discovery on cross media topics.