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.
Introduction
Mining of User Generated Content and Its Applications Marie-Francine Moens, Juanzi Li, and Tat-Seng Chua
The Web and Web Trends
Defining User Generated Content
A Brief History of Creating, Searching and Mining User Generated Content
Goals of the Book
User Generated Content: Concepts and Bottlenecks
Organization of the Book
Mining User Generated Content: Broader Context
Mining Different Media
Social Annotation Jia Chen, Shenghua Bao, Haofen Wang, and Yong Yu
Research on Social Annotations
Techniques in Social Annotations
Application of Social Annotations
Sentiment Analysis in UGC Ning Yu
Introduction
Background
Major Issues in Sentiment Analysis
Mining User Generated Data for Music Information Retrieval Markus Schedl, Mohamed Sordo, Noam Koenigstein, and Udi Weinsberg
Introduction to Music Information Retrieval
Web Pages
Microblogs
Explicit User Ratings
Peer-to-Peer Networks
Social Tags
Social Networks
Conclusions
Graph and Network Pattern Mining Jan Ramon, Constantin Comendant, Mostafa Haghir Chehreghani, and Yuyi Wang
Introduction
Basic Concepts
Transactional Graph Pattern Mining
Single Network Mining
Concluding Remarks
Additional Reading
Glossary
Mining and Searching Different Types of UGC
Knowledge Extraction from Wiki/BBS/Blogs/News Websites Jun Zhao, Kang Liu, Guangyou Zhou, Xianpei Han, Zhenyu Qi, and Yang Liu
Introduction
Entity Recognition and Expansion
Relation Extraction
Named Entity Disambiguation
Conclusion
User Generated Content Search Roi Blanco, Manuel Eduardo Ares Brea, and Christina Lioma
Introduction
Overview of State of the Art
Social Tags for Query Expansion
Conclusions
Annotating Japanese Blogs with Syntactic and Affective Information Michal Ptaszynski, Yoshio Momouchi, Jacek Maciejewski, Pawel Dybala, Rafal Rzepka, and Kenji Araki
Introduction
Related Research
YACIS Corpus Compilation
YACIS Corpus Annotation
Applications
Discussion
Conclusions and Future Work
Applications
Question Answering of UGC Chin-Yew Lin
Introduction
Question Answering by Searching Questions?
Question Search
Question Quality, Answer Quality, and User Expertise
Summary
Summarization of UGC Dominic Rout and Kalina Bontcheva
Introduction
Automatic Text Summarization: A Brief Overview
Why Is User Generated Content a Challenge?
Text Summarization of UGC
Structured, Sentiment-Based Summarization of UGC
Keyword-based Summarization of UGC
Visual Summarization of UGC
Evaluating UGC Summaries
Outstanding Challenges
Summary
Recommender Systems Claudio Lucchese, Cristina Ioana Muntean, Raffaele Perego, and Fabrizio Silvestri
Recommendation Techniques
Exploiting Query Logs for Recommending Related Queries
Exploiting Photo Sharing and Wikipedia for Touristic Recommendations
Exploiting Twitter and Wikipedia for News Recommendation
Recommender Systems for Tags
Conclusions
Conclusions and a Roadmap for Future Developments Marie-Francine Moens, Juanzi Li, and Tat-Seng Chua
Summary of the Main Findings
Roadmap
Bibliography
Overview
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 ...