Mining User Generated Content

Marie-Francine Moens, Juanzi Li, Tat-Seng Chua

January 28, 2014 by Chapman and Hall/CRC
Reference - 474 Pages - 47 B/W Illustrations
ISBN 9781466557406 - CAT# K15468
Series: Social Media and Social Computing


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  • Describes how to mine various media, including social annotation, music information retrieval, and networks
  • Covers the mining and searching of different types of UGC, such as Wikis and blogs
  • Presents many applications of UGC, including the use of UGC to answer questions and summarize information
  • Provides a road map for future developments


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.