Mining User Generated Content

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.

Table of Contents

Part I: Introduction
Chapter 1: Mining User Generated Content and Its Applications

Part II: Mining Different Media
Chapter 2: Social Annotation
Chapter 3: Sentiment Analysis in UGC
Chapter 4: Mining User Generated Data for Music Information Retrieval
Chapter 5: Graph and Network Pattern Mining

Part III: Mining and Searching Different Types of UGC
Chapter 6: Knowledge Extraction from Wikis/BBS/Blogs/News Web Sites
Chapter 7: User Generated Content Search
Chapter 8: Annotating Japanese Blogs with Syntactic and Affective Information

Part IV: Applications
Chapter 9: Question-Answering of UGC
Chapter 10: Summarization of UGC
Chapter 11: Recommender Systems
Chapter 12: Conclusions and a Road Map for Future Developments