Social Data Analytics

The book provides an introduction to social data analytics along with its challenges and opportunities. It chiefly focuses on concepts, techniques and methods for organizing, curating, processing and analyzing big social data: from text to image and video analytics. It also provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery. It covers a large body of knowledge on to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics. It provides an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics. The key highlights of the book are that it covers a vast amount of literature to help practitioners and researchers understand the problems, concepts, methods and tools involved in modern social data analytics. It also includes a wealth of material to choose from for courses in data science and analytics. The book provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry. This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on concepts, techniques and methods for organizing, curating, processing, analyzing, and visualizing big social data: from text to image and video analytics. It provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery. The book covers a large body of knowledge that will help practitioners and researchers in understanding the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics. It also provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry. It provides a deep knowledge in social data analytics by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics. The book covers a large body of knowledge to help practitioners and researchers understand the problems, concepts, methods and tools involved in modern social data analytics. The book includes a wealth of material to choose from for courses in data science and analytics. Cover 1 Title Page 2 Copyright Page 3 Dedication 4 Preface 6 Table of Contents 10 Foreword 5 1. Social Data Analytics: Challenges and Opportunities 14 1.1 Understanding Social Data 14 1.2 Organizing Social Data 16 1.2.1 Social Data Volume 17 1.2.2 Social Data Variety 17 1.2.3 Social Data Velocity 18 1.2.4 Social Data and Metadata 19 1.3 Curating Social Data 20 1.4 Processing Social Data 20 1.5 Summarizing Social Data 22 1.6 Storytelling with Social Data 24 1.7 Social Media Text Analytics 25 1.8 Social Image and Video Data Analytic 26 1.9 The Future of Personalization 29 1.10 Social Data Analytics Applications 31 1.11 Goals, Structure, and Organization 34 2. Organizing Social Data 37 2.1 From Data to Big Data 37 2.1.1 Big Data 38 2.1.2 NoSQL: The Need for New Database Management Systems 39 2.2 Capturing Social Data 42 2.3 Organizing Social Data 44 2.4 Warehousing Social Data 45 2.5 Social Data Provenance 46 2.5.1 Provenance Representation 48 2.5.2 Temporal Databases and Graphs 49 2.6 Data Lakes 49 2.6.1 Data Lake as a Service 50 2.6.2 Index and Federated Search 51 2.6.3 Security and Access Control 53 2.7 Concluding Remarks and Discussion 53 3. Curating Social Data 55 3.1 Social Data Curation: Cleaning, Integration, and Transformation 55 3.1.1 Identifying Relevant Data Sources 56 3.1.2 Ingesting Data and Knowledge 57 3.1.3 Data Cleaning 58 3.1.4 Data Integration 60 3.1.5 Data Transformation 61 3.2 Social Data Curation: Adding Value 62 3.2.1 Extraction 64 3.2.2 Correction and Enrichment 67 3.2.3 Linking 69 3.2.4 Summarization 71 3.3 Knowledge Lakes 71 3.4 Concluding Remarks and Discussion 72 4. Social Media Text Analytics 75 4.1 Text Analytics: Overview 75 4.1.1 Text Preprocessing 76 4.1.2 Text Representation 77 4.1.3 Knowledge Discovery 82 4.2 Social Data Text Analytics: Challenges and Opportunities 85 4.2.1 Time Sensitivity 85 4.2.2 Format and Style 86 4.3 Social Data Text Analytics 87 4.3.1 Event Detection 87 4.3.2 Social Data Tagging 88 4.3.3 Topic Modeling 88 4.3.4 Social Data Text Classification 90 4.3.5 Sentiment and Opinion Extraction 91 4.3.6 Linking Textual Data and Social Metadata 92 4.4 Concluding Remarks and Discussion 93 5. Social Media Image and Video Analytics 94 5.1 Image and Video Analytic: Overview 94 5.2 Image and Video Analytic: Opportunities and Challenges 96 5.2.1 Opportunities 97 5.2.2 Challenges 98 5.3 Image and Video Detection and Recognition 99 5.3.1 Object Detection in Images and Video Frames 99 5.3.2 Face Detection and Recognition 105 5.4 Storytelling with Image and Video Data 107 5.4.1 Image and Video Captioning 107 5.4.2 Location Identification 109 5.5 3D Posts on Social Media 109 5.5.1 3D Content Sharing 109 5.5.2 Light Field Technology 110 5.6 Concluding Remarks and Discussion 114 6. Summarizing Social Data 116 6.1 Automatic Text Summarization: Overview 116 6.1.1 Text Summarization v. Text Compression 117 6.2 Social Data Summarization: Challenges and Opportunities 118 6.3 Social Data Summarization: Generic Approaches 119 6.3.1 Abstractive Summarization 121 6.3.2 Extractive Summarization 122 6.3.3 Hybrid Extractive and Abstractive Summarization 126 6.3.4 Structured Summarization 126 6.3.5 Interactive and Personalized Summarization 128 6.4 Micro-blog Data Summarization 129 6.4.1 Time-aware Summarization 129 6.4.2 Event-based Summarization 131 6.4.3 Opinion-based Summarization 131 6.5 Evaluation Techniques 133 6.6 Concluding Remarks and Discussion 134 7. Storytelling with Social Data 136 7.1 Storytelling with Social Data: Overview 136 7.1.1 Challenges and Opportunities 137 7.2 Data-driven Storytelling via Visualization 139 7.2.1 Defining Objectives and Knowing the Audience 140 7.2.2 Identifying a Compelling Narrative 140 7.2.3 Incorporating Key Elements 140 7.2.4 Transparency 140 7.2.5 Visualization Method 141 7.3 Visualization Techniques 141 7.3.1 Static Data Visualization 141 7.3.2 Interactive Data Visualization 142 7.3.3 Adaptive Data Visualization 145 7.4 Concluding Remarks and Discussion 147 8. Social Data and Recommender Systems: The Future of Personalization 148 8.1 Introduction 148 8.1.1 Overview of Recommendation Approaches 149 8.1.2 Collaborative Filtering Approaches 150 8.1.3 Content-Based Approaches 152 8.2 Social Recommendation and Personalization 153 8.2.1 Social Data 154 8.2.2 Trust-aware Recommendation 155 8.2.3 Context-aware Recommendation 156 8.2.4 Temporal Recommendation 157 8.2.5 Cross-Domain Recommendation 157 8.2.6 Group Recommendation 158 8.3 Bias in Social Recommendation 159 8.4 Application Domains 161 8.4.1 Video Domain 161 8.4.2 Music Domain 161 8.4.3 Fashion Domain 162 8.4.4 Tourism Domain 163 8.4.5 Food Domain 163 8.5 Concluding Remarks and Discussion 164 9. Social Data Analytics Applications 165 9.1 Social Data and Trust 165 9.2 Bias in Social Data 166 9.3 Personality Detection from Social Data 167 9.4 Sentiment Analysis of Social Data 169 9.5 Personalization with Social Data 170 9.6 Sales and Marketing: Creating Successful Campaigns with Social Media Marketing Analytics 172 9.7 Influence Maximization: Identify Influencers for Brands and Industries 173 9.8 Situational Awareness: Discover Trending Topics 174 9.9 Social Media Information Discovery: From Topic Trends to Sentiment Ratio 176 9.10 Linking Social Media Performance to Business and Revenue Growth 177 9.11 Performance Analysis of the Industry 178 9.12 Concluding Remarks and Discussion 180 References 181 Index 250 Social,Data,Provenance;,Trust,Prediction;,Big,Data,Summarization;,Data,Curation Social Data Provenance,Trust Prediction,Big Data Summarization,Data Curation "The book provides an introduction to social data analytics along with its challenges and opportunities. It chiefly focuses on concepts, techniques and methods for organizing, curating, processing and analyzing big social data: from text to image and video analytics. It also provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery. It covers a large body of knowledge on to help practitioners and researchers understand the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics. The book provides researchers with an introduction to these fields by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics. The key highlights of the book are that it covers a large body of knowledge to help practitioners and researchers understand the problems, concepts, methods and tools involved in modern social data analytics. It also includes a wealth of material to choose from for courses in data science and analytics. The book provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry"-- Provided by publisher https://www.routledge.com/9781032196275 This book is an introduction to social data analytics along with its challenges and opportunities in the age of Big Data and Artificial Intelligence. It focuses primarily on concepts, techniques and methods for organizing, curating, processing, analyzing, and visualizing big social data: from text to image and video analytics. It provides novel techniques in storytelling with social data to facilitate the knowledge and fact discovery. The book covers a large body of knowledge that will help practitioners and researchers in understanding the underlying concepts, problems, methods, tools and techniques involved in modern social data analytics. It also provides real-world applications of social data analytics, including: Sales and Marketing, Influence Maximization, Situational Awareness, customer success and Segmentation, and performance analysis of the industry. It provides a deep knowledge in social data analytics by comprehensively classifying the current state of research, by describing in-depth techniques and methods, and by highlighting future research directions. Lecturers will find a wealth of material to choose from for a variety of courses, ranging from undergraduate courses in data science to graduate courses in data analytics.