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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
[Place of publication not identified] :
CRC Press,
2021.
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| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Muhlmeyer, Michael. | |
| 245 | 1 | 0 | |a Information spread in a social media age : |b modeling and control. |
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| 520 | |a 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, model, and analyze the trajectory of information spread within this new paradigm has never been greater. This text expands upon the authors' combined teaching experience, engineering knowledge, and multiple academic journal publications on these topics to present an intuitive and easy to understand exploration of social media information spread alongside the technical and mathematical concepts. By design, this book uses simple language and accessible and modern case studies (including those centered around United States mass shootings, the #MeToo social movement, and more) to ensure it is accessible to the casual reader. At the same time, readers with prior knowledge of the topics will benefit from the mathematical model and control elements and accompanying sample simulation code for each main topic. By reading this book and working through the included exercises, readers will gain a general understanding of modern social media systems, network fundamentals, model development techniques, and social marketing. The mathematical modeling of information spread over social media is heavily emphasized through a review of existing epidemiology and marketing based models. The book then presents novel models developed by the authors to account for modern social media concerns such as community filter bubbles, strongly polarized groups, and contentious information spread. Readers will learn how to build and execute simple case studies using Twitter data to help verify the text's proposed models. Once the reader is armed with a fundamental understanding of mathematical modeling and social media-based system considerations, the book introduces more complex engineering control concepts, including controller design, PID control, and optimal control. Examples of control methods for social campaigns and misinformation mitigation applications are covered in a step-by-step format from problem formulation to solution simulation and results discussions. While many of the examples and methods are framed in the context of controlling social media information spread, the material is also directly applicable to many different types of controllable systems. With the essential background, models, and tools presented within, any interested reader can take the first steps toward exploring and taming the growing complexity of the modern social media age. | ||
| 545 | 0 | |a Michael Muhlmeyer provides engineering consulting services at Sabre Engineering Consulting in Los Angeles, CA. His areas of interest include mathematical modeling, control systems, computational social systems, information spread on social media, fake news, and novel applications of engineering to multidisciplinary research. Shaurya Agarwal is currently an Assistant Professor in the Civil, Environmental, and Construction Engineering Department at the University of Central Florida. His research focuses on interdisciplinary areas of cyber-physical systems, smart and connected communities, and socio-technical-infrastructures systems. | |
| 505 | 0 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 505 | 8 | |a 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. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Social media |x Mathematical models. | |
| 650 | 0 | |a Online social networks |x Mathematical models. | |
| 650 | 0 | |a Information technology |x Mathematical models. | |
| 650 | 0 | |a Disinformation |x Prevention. | |
| 650 | 6 | |a Médias sociaux |x Modèles mathématiques. | |
| 650 | 6 | |a Réseaux sociaux (Internet) |x Modèles mathématiques. | |
| 650 | 6 | |a Technologie de l'information |x Modèles mathématiques. | |
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| 700 | 1 | |a Agarwal, Shaurya. | |
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