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

By: Roy, Suman Deb.
Contributor(s): Zeng, Wenjun.
Material type: TextTextSeries: eBooks on Demand.Publisher: Dordrecht : Springer, 2014Description: 1 online resource (181 p.).ISBN: 9783319091174.Subject(s): Application software -- Development | Online social networks -- Design | Social media | Web site developmentGenre/Form: Electronic books.Additional physical formats: Print version:: Social Multimedia Signals : A Signal Processing Approach to Social Network PhenomenaDDC classification: 006.754 LOC classification: HM742Online resources: Click here to view this ebook.
Contents:
Preface; Contents; 1 Web 2.x; References; 2 Media on the Web; 2.1 Foundational Questions of Multimedia Research; 2.2 Angles into Social Multimedia Data; 2.3 Adoption of Social Media in Our Digital Lives; References; 3 The World of Signals; 3.1 Signals; 3.2 Signal Processing; 3.2.1 Examples of Signal Processing Approaches; 3.2.2 A Word on Big Data, and Why Signal Processing Techniques Have Become More Imperative Than Ever; 3.3 Social Multimedia Signals; References; 4 The Network and the Signal; 4.1 Trails and Ripples; 4.2 Structure Signal-The Trail of a Random Walker
4.3 Trends Signal-The Trail of Pseudo-random Social Activity4.4 Events Signal-The Ripple Across Domains; References; 5 Detection: Needle in a Haystack; 5.1 Signal Detection Basics; 5.1.1 Hypothesis Testing; 5.1.2 Discrete Time Signal Detection; 5.1.3 Sequential Signal Detection; 5.2 Social Media Signal Detection; 5.3 Social Media Data Sampling; 5.3.1 Trend Volatility Signal Sampling; 5.4 Social Media Data Filtering; 5.4.1 Spike-based Filtering; 5.5 The Importance of Signal Origins; References; 6 Estimation: The Empirical Judgment; 6.1 Signal Parameters; 6.1.1 Geospan; 6.1.2 Persistence
6.1.3 Recurrence6.2 Estimators; 6.2.1 Bayes Estimator; 6.2.2 Bayesian Network; 6.2.3 Maximum Likelihood Sequence Estimation; 6.2.4 Estimation in Latent Dirichlet Allocation; 6.3 What Is a Good Signal?; References; 7 Following Signal Trajectories; 7.1 Time Series Analysis; 7.1.1 Wave Patterns; 7.1.2 Predict the Future Time Series; 7.1.3 Analyzing Causality in Series; 7.2 Spatio-Temporal Evolution of Trends; 7.2.1 Volatility Signals; 7.2.2 Dependency Among Trend Variables; 7.2.3 Path Analysis; 7.3 Attention Automaton; 7.3.1 Attention Shift Tendencies; 7.3.2 Modeling Categorical Affinity
7.3.2.1 Geographical Trend Initiation7.3.2.2 Follower Affinity; 7.3.2.3 User Group Categorical Affinity; 7.3.3 Modeling Attention Shifts; 7.3.4 Evaluation of the Attention Automaton; 7.3.4.1 User Groups by Geographical Locations; 7.3.4.2 User Groups by Brand Following; 7.4 Conclusion; References; 8 Capturing Cross-Domain Ripples; 8.1 The Ripple Phenomenon; 8.2 Learning Topics from Streaming Data; 8.2.1 Topic Spaces; 8.2.2 Topic Space as the Bridge; 8.2.3 Runtimes of OSLDA; 8.3 SocialTransfer; 8.3.1 Transfer Graph; 8.3.2 Learning Transfer Graph Spectra; 8.3.3 Incorporating Social Topics
8.3.4 Algorithm for SocialTransferReferences; 9 Socially Aware Media Applications; 9.1 Socially Aware Video Recommendation; 9.1.1 Experiments with Social Video Recommendation; 9.2 Social Video Popularity Prediction; 9.2.1 Modeling Social Prominence of Media; 9.2.2 Experiments with Social Video Popularity; 9.3 Socialized Query Suggestion; 9.3.1 What the Data Shows; 9.4 Parameter Tuning; References; 10 Revelations from Social Multimedia Data; References; 11 Socio-Semantic Analysis; 11.1 Semantic Web Data; 11.2 Building a Concept Graph-SemNet
11.3 Categorical Classification of Trending Topics Using Concept Graph
Summary: 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 me
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Preface; Contents; 1 Web 2.x; References; 2 Media on the Web; 2.1 Foundational Questions of Multimedia Research; 2.2 Angles into Social Multimedia Data; 2.3 Adoption of Social Media in Our Digital Lives; References; 3 The World of Signals; 3.1 Signals; 3.2 Signal Processing; 3.2.1 Examples of Signal Processing Approaches; 3.2.2 A Word on Big Data, and Why Signal Processing Techniques Have Become More Imperative Than Ever; 3.3 Social Multimedia Signals; References; 4 The Network and the Signal; 4.1 Trails and Ripples; 4.2 Structure Signal-The Trail of a Random Walker

4.3 Trends Signal-The Trail of Pseudo-random Social Activity4.4 Events Signal-The Ripple Across Domains; References; 5 Detection: Needle in a Haystack; 5.1 Signal Detection Basics; 5.1.1 Hypothesis Testing; 5.1.2 Discrete Time Signal Detection; 5.1.3 Sequential Signal Detection; 5.2 Social Media Signal Detection; 5.3 Social Media Data Sampling; 5.3.1 Trend Volatility Signal Sampling; 5.4 Social Media Data Filtering; 5.4.1 Spike-based Filtering; 5.5 The Importance of Signal Origins; References; 6 Estimation: The Empirical Judgment; 6.1 Signal Parameters; 6.1.1 Geospan; 6.1.2 Persistence

6.1.3 Recurrence6.2 Estimators; 6.2.1 Bayes Estimator; 6.2.2 Bayesian Network; 6.2.3 Maximum Likelihood Sequence Estimation; 6.2.4 Estimation in Latent Dirichlet Allocation; 6.3 What Is a Good Signal?; References; 7 Following Signal Trajectories; 7.1 Time Series Analysis; 7.1.1 Wave Patterns; 7.1.2 Predict the Future Time Series; 7.1.3 Analyzing Causality in Series; 7.2 Spatio-Temporal Evolution of Trends; 7.2.1 Volatility Signals; 7.2.2 Dependency Among Trend Variables; 7.2.3 Path Analysis; 7.3 Attention Automaton; 7.3.1 Attention Shift Tendencies; 7.3.2 Modeling Categorical Affinity

7.3.2.1 Geographical Trend Initiation7.3.2.2 Follower Affinity; 7.3.2.3 User Group Categorical Affinity; 7.3.3 Modeling Attention Shifts; 7.3.4 Evaluation of the Attention Automaton; 7.3.4.1 User Groups by Geographical Locations; 7.3.4.2 User Groups by Brand Following; 7.4 Conclusion; References; 8 Capturing Cross-Domain Ripples; 8.1 The Ripple Phenomenon; 8.2 Learning Topics from Streaming Data; 8.2.1 Topic Spaces; 8.2.2 Topic Space as the Bridge; 8.2.3 Runtimes of OSLDA; 8.3 SocialTransfer; 8.3.1 Transfer Graph; 8.3.2 Learning Transfer Graph Spectra; 8.3.3 Incorporating Social Topics

8.3.4 Algorithm for SocialTransferReferences; 9 Socially Aware Media Applications; 9.1 Socially Aware Video Recommendation; 9.1.1 Experiments with Social Video Recommendation; 9.2 Social Video Popularity Prediction; 9.2.1 Modeling Social Prominence of Media; 9.2.2 Experiments with Social Video Popularity; 9.3 Socialized Query Suggestion; 9.3.1 What the Data Shows; 9.4 Parameter Tuning; References; 10 Revelations from Social Multimedia Data; References; 11 Socio-Semantic Analysis; 11.1 Semantic Web Data; 11.2 Building a Concept Graph-SemNet

11.3 Categorical Classification of Trending Topics Using Concept Graph

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 me

Description based upon print version of record.

Author notes provided by Syndetics

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.

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