Social Computing with Artificial Intelligence

Description

https://www.springer.com/gp/book/9789811577598 This book provides a comprehensive introduction to the application of artificial intelligence in social computing, from fundamental data processing to advanced social network computing. To broaden readers’ understanding of the topics addressed, it includes extensive data and a large number of charts and references, covering theories, techniques and applications. It particularly focuses on data collection, data mining, artificial intelligence algorithms in social computing, and several key applications of social computing application, and also discusses network propagation mechanisms and dynamic analysis, which provide useful insights into how information is disseminated in online social networks. This book is intended for readers with a basic knowledge of advanced mathematics and computer science. Preface Contents 1 Introduction 1.1 Research Background 1.2 Mainstreams of Research Field 1.2.1 Social Data Perception and Knowledge Discovery 1.2.2 Community Modeling of Individuals and Groups 1.2.3 Information Dissemination and Dynamic Evolution of Network 1.2.4 Decision Support and Application 1.3 Structure of This Book References Part IData 2 Data Collection 2.1 Data Types and Sources 2.2 Data Collection and Tools 2.2.1 Data Acquisition 2.2.2 Common Data Processing Toolkit 3 Data Processing Methodology 3.1 Data Processing Principles 3.1.1 Behavior Tracking 3.2 Data Processing Methods Part IIAI Models and Algorithms 4 Supervised and Unsupervised Learning Models 4.1 Supervised Learning Models 4.1.1 Generalized Linear Algorithms 4.1.2 Decision Trees 4.1.3 Nearest Neighbors 4.1.4 Bayesian Methods 4.1.5 Bayesian Regression 4.1.6 Gaussian Processes 4.1.7 Support Vector Machines 4.1.8 Ensemble Methods 4.2 Unsupervised/Semi-supervised Learning Models 4.2.1 Dimensionality Reduction Algorithms 4.2.2 Clustering Algorithms References 5 State-of-the-Art Artificial Intelligence Algorithms 5.1 Deep Learning 5.1.1 Deep Neural Network 5.1.2 Convolutional Neural Network 5.1.3 Recurrent Neural Network and Long Short-Term Network 5.2 Reinforcement Learning 5.2.1 Reinforcement Learning Algorithm 5.2.2 Deep Reinforcement Learning 5.3 Brother Learning 5.4 Epiphany Learning 5.4.1 The Concept of Epiphany 5.4.2 Learning with Teachers, Learning without Teachers, and Epiphany Learning 5.4.3 Structured Description of Epiphany Learning References 6 Social Network Data Mining and Knowledge Discovery 6.1 Online Social Networks Text Processing Models 6.1.1 Information Extraction 6.1.2 Keywords Mining 6.1.3 Topic Detection and Tracking 6.1.4 Automatic Summarization 6.1.5 Sentiment Analysis 6.2 Online Social Networks Image Recognition Models 6.2.1 Image Retrieval 6.2.2 Image Object Detection and Classification References 7 Social Networks Structure Analysis and Online Community Discovery 7.1 Online Social Networks Topology Structure and Models 7.1.1 Basic Concepts of Network Topology Structure 7.1.2 Regular Networks and Random Graphs 7.1.3 Small World Network Model 7.1.4 Scale-Free Network Model 7.1.5 Local World Evolutionary Network Model 7.2 Online Social Networks Community Discovery Models 7.2.1 Basic Concepts of Community 7.2.2 Online Networks Community Discovery-Related Technologies Model 7.2.3 Evaluation Indicators of Community Discovery Model References 8 Social Network Propagation Mechanism and Online User Behavior Analysis 8.1 Network Propagation Mechanism and Dynamic Analysis 8.1.1 Propagation Critical Value Theory for Complex Networks 8.1.2 Propagation Dynamics of Complex Networks 8.2 Online Crowd Behavior and Influence Model 8.2.1 Measurement of User Node Impact Based on Topological Structure 8.2.2 Impact Measurement Based on Content and Behavior Characteristics 8.3 Online Social Networks User Role Identification 8.3.1 Uncertain Role Recognition Method 8.3.2 Role Recognition References Part IIIApplications 9 Social Computing Application in Public Security and Emergency Management 9.1 Introduction 9.2 Problem Description and Extraction 9.2.1 Qualitative Analysis of Car Hijacking Scenario 9.2.2 Quantitative Description of Information Value of Social Networks 9.3 Social Computing in Emergency Problem 9.3.1 Emergency Decision Support Architecture for Bus Hijacking 9.3.2 Anomaly Detection Algorithm Model Based on SIFT Features and SVM Classification References 10 Social Computing Application in Business Decision Support 10.1 Introduction 10.2 Social Computing in Business Decision Support 10.2.1 Model Overall Framework Diagram 10.2.2 News Type Division Model 10.2.3 Event Research Model References 11 Social Computing Application in Unsupervised Oracle Handwriting Recognition Based on Pic2Vec Image Content Mapping 11.1 Image Feature Extraction Method 11.2 Experiments and Analysis 11.3 Thinking over Oracle Bone Inscriptions 12 Social Computing Application in Online Crowd Behavior and Psychology 12.1 Introduction 12.1.1 Online Social Network Rumors and Traditional Rumors 12.1.2 Key Issues in OSN Rumor Detection 12.1.3 OSN Rumor Detection Open Data Resources 12.2 Content Classification Based on Machine Learning Rumor Detection 12.3 Network Structure Based Rumor Source Detection References Appendix Mathematical Basis A.1 Basis of Vectors and Matrices A.1 1. Vector Basis A.1 2. Matrix Basis A.2 Derivatives and Partial Derivatives A.2 1. Derivatives in Neural Networks A.2 2. Partial Derivatives in Neural Networks A.3 Gradient Descent Method A.3.1 Derivation Process of One-Dimensional Gradient Descent Method A.3 2. Gradient Descent Formula of Binary Function Index