With the popularity of social media platforms such as Facebook and Twitter, the amount of useful data in these sources is rapidly increasing, making them promising places for information acquisition. This research aims at the customized organization of a social media corpus using focused topic hierarchy. It organizes the contents into different structures to meet with users' different information needs (e.g., "iPhone 5 problem" or "iPhone 5 camera"). To this end, we introduce a novel function to measure the likelihood of a topic hierarchy, by which the users' information need can be incorporated into the process of topic hierarchy construction. Using the structure information within the generated topic hierarchy, we then develop a probability based model to identify the representative contents for topics to assist users in document retrieval on the hierarchy. Experimental results on real world data illustrate the effectiveness of our method and its superiority over state-of-the-art methods for both information organization and retrieval tasks.

User generated contents (UGCs) carry a huge amount of high quality information. However, the information overload and diversity of UGC sources limit their potential uses. In this research, we propose a framework to organize information from multiple UGC sources by a topic hierarchy which is automatically generated and updated using the UGCs. We explore the unique characteristics of UGCs like blogs, cQAs, microblogs, etc., and introduce a novel scheme to combine them. We also propose a graph-based method to enable incremental update of the generated topic hierarchy. Using the hierarchy, users can easily obtain a comprehensive, in-depth and up-to-date picture of their topics of interests. The experiment results demonstrate how information from multiple heterogeneous sources improves the resultant topic hierarchies. It also shows that the proposed method achieves better F1 scores in hierarchy generation as compared to the state-of-the-art methods.