Originating from Facebook, LinkedIn, Twitter, Instagram, YouTube, and many other networking sites, the social media shared by users and the associated metadata are collectively known as user generated content (UGC). To analyze UGC and glean insight about user behavior, robust techniques are needed to tackle the huge amount of real-time, multimedia, and multilingual data. Researchers must also know how to assess the social aspects of UGC, such as user relations and influential users. Mining User Generated Content is the first focused effort to compile state-of-the-art research and address future directions of UGC. It explains how to collect, index, and analyze UGC to uncover social trends and user habits. Divided into four parts, the book focuses on the mining and applications of UGC. The first part presents an introduction to this new and exciting topic. Covering the mining of UGC of different medium types, the second part discusses the social annotation of UGC, social network graph construction and community mining, mining of UGC to assist in music retrieval, and the popular but difficult topic of UGC sentiment analysis. The third part describes the mining and searching of various types of UGC, including knowledge extraction, search techniques for UGC content, and a specific study on the analysis and annotation of Japanese blogs. The fourth part on applications explores the use of UGC to support question-answering, information summarization, and recommendations.