Information Spread in a Social Media Age: Modeling and Control

Cover
Half Title
Title Page
Copyright Page
Dedication
Contents
Foreword
Preface
Authors
Acknowledgments
List of Figures
List of Tables
List of Codes
Symbols
1. Introduction
	1.1. Expressions of Information
	1.2. Why Information Spread Matters?
	1.3. Modern Information Spread Scenarios
		1.3.1. Global Communication During a Pandemic
		1.3.2. Governments and Mass Panic
		1.3.3. Shopping and Advertising
		1.3.4. Social or Political Campaigning
		1.3.5. Misinformation, Disinformation, and Fake News
	1.4. Controllable Information Spread
	1.5. How to Read This Book
	1.6. Exercises
I. Understanding Social Networking Systems
	2. Social Media in Popular Culture
		2.1. The Topology of Social Media
		2.2. Social Networking Sites
			2.2.1. Twitter
			2.2.2. Facebook
			2.2.3. LinkedIn
		2.3. Content Sharing Sites
		2.4. Discussion Forums
		2.5. News and Blogs
		2.6. Shopping and Reviews
		2.7. Games and Music
		2.8. Hybrid Social Media
			2.8.1. Internet Memes
		2.9. Exercises
	3. Social Theory and Networks
		3.1. Philosophy, Science, and Information Spread
			3.1.1. The Ancient World
			3.1.2. The Medieval World
			3.1.3. The Early Modern World
			3.1.4. The Contemporary World
		3.2. Social Theory and Social Networks
		3.3. Social Exchange Theory
		3.4. Exercises
	4. Social Network Relationships and Structures
		4.1. Social Network Relationship Overview
		4.2. Core Social Network Relationships
			4.2.1. Symmetry
			4.2.2. Directionality
			4.2.3. Intermediary Relationships
			4.2.4. Complex Networks
		4.3. Homophily and Filter Bubbles
		4.4. Dyadic Relationships and Reciprocity
		4.5. Triads and Balanced Relationships
		4.6. Social Network Analysis Software
		4.7. Exercises
	5. Social Network Analysis
		5.1. Density and Structural Holes
		5.2. Weak and Strong Ties
		5.3. Centrality and Distance
		5.4. Small World Networks
		5.5. Clusters, Cohesion, and Polarization
		5.6. The Adjacency Matrix
			5.6.1. Example: A Fencing Club Sociogram
		5.7. Exercises
II. Macroscopic Modeling and Information Spread
	6. Modeling Basics
		6.1. What is a Model?
		6.2. Models in Decision Making
		6.3. Standard Models
		6.4. Models, Assumptions, and Approximations
		6.5. Mathematical Systems Modeling
		6.6. Microscopic and Macroscopic Models
		6.7. Basic Steps to Develop a Mathematical Model
		6.8. Model Validation
		6.9. Modeling and the State-Space Representation
		6.10. Example 1: A Spring-Mass System
		6.11. Example 2: A Predator-Prey System
		6.12. Example 3: An RLC Circuit
		6.13. Example 4: An Epidemic Model
		6.14. Example 5: Vehicular Tra c Modeling
			6.14.1. LWR and Greenshields' Models for Tra c
			6.14.2. ODE Approximation of LWR Model
		6.15. Exercises
	7. Epidemiology-Based Models for Information Spread
		7.1. Epidemiology Models
			7.1.1. The SIR Disease Spread Model
			7.1.2. The SEIR Disease Spread Model
			7.1.3. Herd Immunity in Epidemiology
			7.1.4. \Flattening the Curve"
			7.1.5. Epidemiology Models as Analog Models
		7.2. Information Spread Models: Overview and Conventions
		7.3. The Ignorant-Spreader Model
		7.4. The Ignorant-Spreader-Ignorant (ISI) Model
		7.5. The Ignorant-Spreader-Recovered (ISR) Model
		7.6. Reproductive Number and Herd Immunity
		7.7. ISR Model for Social Media
			7.7.1. ISR Model for Social Media with Decay
		7.8. ISCR Model for Contentious Information Spread
		7.9. Hybrid ISCR Model
		7.10. ISSRR Model for Contentious Information
		7.11. Exercises
	8. Stochastic Modeling of Information Spread
		8.1. Brownian Motion
		8.2. Deterministic and Stochastic Realizations of Processes
		8.3. Stochastic Modeling Considerations for Social Media Systems
		8.4. Stochastic ISI Information Model
		8.5. Stochastic ISR Information Modeling and Social Media
		8.6. Exercises
	9. Social Marketing-Based Models for Information Spread
		9.1. Vidale-Wolfe Model
		9.2. Bass Model
		9.3. Sethi Model
		9.4. Event-triggered Social Media Chatter Model
			9.4.1. Socio-Equilibrium Threshold
			9.4.2. Simulation and Discussion
		9.5. Exercises
	10. Case Studies
		10.1. Selecting Case Studies
		10.2. Case Study-1: 2017. Mass Shootings
			10.2.1. Data Acquisition
			10.2.2. Parameter Estimation
			10.2.3. Results and Discussion
		10.3. Case Study-2: The #MeToo Social Movement
			10.3.1. Data Acquisition
			10.3.2. Parameter Estimation
			10.3.3. Results and Discussion
		10.4. Case Study-3: 2018. Golden Globe Awards
			10.4.1. Data Acquisition
			10.4.2. Parameter Estimation
			10.4.3. Results and Discussion
		10.5. Case Study-4: Viral Internet Debates
			10.5.1. Data Acquisition
			10.5.2. Parameter Estimation
			10.5.3. Results and Discussion
		10.6. Exercises
III. Control Methods For Information Spread
	11. Control Basics
		11.1. Introduction
		11.2. Open-loop and Closed-loop Control Systems
		11.3. SISO and MIMO Control Systems
		11.4. Continuous-time and Discrete-time Control Systems
		11.5. Control System Design
	12. Control Methods
		12.1. State Variable Feedback Controller
			12.1.1. Full-state Feedback Control Design
			12.1.2. Observer Design
			12.1.3. Full-state Feedback Controller and Observer
		12.2. PID Controller
		12.3. Optimal Control
			12.3.1. Performance Measure
			12.3.2. Dynamic Programming and Principle of Optimality
			12.3.3. Pontryagin's Minimization Principle
			12.3.4. Illustrative Example
		12.4. Exercises
	13. Information Spread and Control
		13.1. Controlling Socio-technical Systems
		13.2. The Control Action and Social Media Systems
		13.3. Optimal Control and Social Media
		13.4. Exercises
	14. Control Application 1: Advertisements and Social Crazes
		14.1. Scenario Description
		14.2. Problem Formulation
		14.3. Dynamic Programming Approach
		14.4. Pontryagin's Approach
		14.5. Numerical Solution and Discussion
	15. Control Application 2: Stopping a Fake News Outbreak
		15.1. Scenario Description
		15.2. Problem Formulation
		15.3. Pontryagin's Approach
		15.4. Numerical Solution and Discussion
	16. Concluding Thoughts
		16.1. What Have We Learned?
		16.2. But Now What?
		16.3. The Future and Beyond
Bibliography
Index