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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: Cham : Springer, 2014Copyright date: ©2015Edition: 1st ed.Description: 1 online resource (181 pages).Content type: text Media type: computer Carrier type: online resourceISBN: 9783319091174.Subject(s): Online social networks -- Design.;Application software -- DevelopmentGenre/Form: Electronic books.Additional physical formats: Print version:: Social Multimedia Signals : A Signal Processing Approach to Social Network PhenomenaDDC classification: 006.7 LOC classification: TA1-2040Online resources: Click here to view this ebook.
Contents:
Intro -- 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 Activity -- 4.4 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 Recurrence -- 6.2 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 Initiation -- 7.3.2.2 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 SocialTransfer -- References -- 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 -- 11.4 Finding Topic Coherency Using Semantic Graphs -- 11.4.1 What Experiments Tell Us -- 11.5 A Semantic Way of Separating Signal from the Noise -- References -- 12 Data Visualization: Gazing at Ripples -- 12.1 Types of Visualization -- 12.2 From Data to Stories Through Visualization -- References -- Appendix.
Summary: 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.
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Intro -- 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 Activity -- 4.4 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 Recurrence -- 6.2 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 Initiation -- 7.3.2.2 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 SocialTransfer -- References -- 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 -- 11.4 Finding Topic Coherency Using Semantic Graphs -- 11.4.1 What Experiments Tell Us -- 11.5 A Semantic Way of Separating Signal from the Noise -- References -- 12 Data Visualization: Gazing at Ripples -- 12.1 Types of Visualization -- 12.2 From Data to Stories Through Visualization -- References -- Appendix.

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.

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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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