Information spread in a social media age : modeling and control.

The rise of social networks and social media has led to a massive shift in the ways information is dispersed. Platforms like Twitter and Facebook allow people to more easily connect as a community, but they can also be avenues for misinformation, fake news, and polarization. The need to examine, mod...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Muhlmeyer, Michael
Otros Autores: Agarwal, Shaurya
Formato: Electrónico eBook
Idioma:Inglés
Publicado: [Place of publication not identified] : CRC Press, 2021.
Temas:
Social media > Mathematical models.
Online social networks > Mathematical models.
Information technology > Mathematical models.
Disinformation > Prevention.
Médias sociaux > Modèles mathématiques.
Réseaux sociaux (Internet) > Modèles mathématiques.
Technologie de l'information > Modèles mathématiques.
COMPUTERS > Networking > General.
TECHNOLOGY > Electricity.
Information technology > Mathematical models
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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.

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