Part I: Social Relationships in Human-Centered Media -- Bridging Human-Centered Social Media Content across Web Domains -- Learning Social Relations from Videos -- Community Understanding in Location-Based Social Networks -- Social Role Recognition for Human Event Understanding -- Integrating Randomization and Discrimination for Classifying Human-Object Interaction Activities -- Part II: Human Attributes in Social Media Analytics -- Recognizing People in Social Context -- Female Facial Beauty Attribute Recognition and Editing -- Facial Age Estimation -- Identity and Kinship Relations in Group Pictures -- Recognizing Occupations through Probabilistic Models.

Utilizing the ubiquity of social media in modern society, the emerging interdisciplinary field of social computing offers the promise of important human-centered applications. Human-Centered Social Media Analytics provides a timely and unique survey of next-generation social computational methodologies. The text explains the fundamentals of this field, and describes state-of-the-art methods for inferring social status, relationships, preferences, intentions, personalities, needs, and lifestyles from human information in unconstrained visual data. The collected chapters present a range of different viewpoints examining the various possibilities and challenges to machine understanding of humans in a social context. Topics and features: Includes perspectives from an international and interdisciplinary selection of pre-eminent authorities Presents balanced coverage of both detailed theoretical analysis and real-world applications Examines social relationships in human-centered media for the development of socially-aware video, location-based, and multimedia applications Reviews techniques for recognizing the social roles played by people in an event, and for classifying human-object interaction activities Discusses the prediction and recognition of human attributes via social media analytics, including social relationships, facial age and beauty, and occupation Requires no prior background knowledge of the area This authoritative text/reference will be a valuable resource for researchers and graduate students interested in social media and networking, computer vision and biometrics, big data, and HCI. Practitioners in these fields, as well as in image processing and computer graphics, will also find the book of great interest. Dr. Yun Fu is an assistant professor in the Department of Electrical and Computer Engineering at Northeastern University, Boston, MA, USA, where he is the founder of the Synergetic Media Learning (SMILE) Lab.