Springer Social Multimedia Signals: A Signal Processing Approach to Social Network Phenomena

This book provides a comprehensive coverage of the state-of-the-art in understanding media popularity and trends in online social networks through social multimedia signals. With insights from the study of popularity and sharing patterns of online media, trend spread in social media, social network analysis for multimedia and visualizing diffusion of media in online social networks. In particular, the book will address the following important issues: Understanding social network phenomena from a signal processing point of view; The existence and popularity of multimedia as shared and social media, how content or origin of sharing activity can affect its spread and popularity; The network-signal duality principle, i.e., how the signal tells us key properties of information diffusion in networks; The social signal penetration hypothesis, i.e., how the popularity of media in one domain can affect the popularity of media in another. The book will help researchers, developers and business (advertising/marketing) individuals to comprehend the potential in exploring social multimedia signals collected from social network data quantitatively from a signal processing perspective. From the Back Cover Social Multimedia Signals is intended for those whose interest is to study the Social Web and develop automated tools to analyze it better. It is especially useful for researchers experienced with signal processing or multimedia analysis but have little exposure to social networks and social multimedia data. Those new to social multimedia should find the first chapters extremely useful to get a thorough look at how social data behaves. Conversely, social scientists should find useful the authors’ introduction to several signal processing techniques that can be employed to manipulate large-scale social data. For those new to signal processing, Chapters 5, 6 and 7 will get readers underway with basic techniques for signal processing from social multimedia. Later chapters include a significant amount of material on machine learning for those interested in intelligent algorithms for the Social Web. The authors wrote this book in a balanced fashion, for multimedia researchers, social scientists, network scientists, data scientists who work with social web data, and professionals who use social media on a daily basis.   ·         Explores how media popularity in one domain is determined by another domain; ·         Presents a granular look at social networks: micro, meso, and macro; ·         Examines finding hidden communities in social networks based on shared multimedia. About the Author Dr. Suman Deb Roy is a Data Scientist with Betaworks, NY Dr. Wenjun (Kevin) Zeng is a Professor at the Computer Science Dept. with the Univ. of Missouri, Columbia, MO, USA.