1st Edition

Information Spread in a Social Media Age Modeling and Control

By Michael Muhlmeyer, Shaurya Agarwal Copyright 2021
    278 Pages 10 Color & 107 B/W Illustrations
    by CRC Press

    278 Pages 10 Color & 107 B/W Illustrations
    by CRC Press

    278 Pages 10 Color & 107 B/W Illustrations
    by CRC Press

    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.

    1 Introduction
    1.1 Expressions of Information
    1.2 Why Information Spread Matters?
    1.3 Modern Information Spread Scenarios
    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
    2.3 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
    2.9 Exercises

    3 Social Theory and Networks
    3.1 Philosophy, Science, and Information Spread
    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
    4.3 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
    5.7 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 Traffic Modeling

    7 Epidemiology-Based Models for Information Spread
    7.1 Epidemiology Models
    7.2 Information Spread Models: Overview and Conventions
    7.3 The Ignorant-Spreader (IS) 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
    7.8 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
    9.5 Exercises

    10 Case Studies
    10.1 Selecting Case Studies
    10.2 Case Study-1: 2017 Mass Shootings
    10.3 Case Study-2: The #MeToo Social Movement
    10.4 Case Study-3: 2018 Golden Globe Awards
    10.5 Case Study-4: Viral Internet Debates
    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
    12.2 PID Controller
    12.3 Optimal Control
    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


    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.