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book cover for Information Spread in a Social Media Age: Modeling and Control

Information Spread in a Social Media Age: Modeling and Control

By Michael Muhlmeyer (Author), Shaurya Agarwal (Author)

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

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.

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Recent Publications (2020 – Present)

  • Huang, A.J. & Agarwal, S. (2023). On the Limitations of Physics-informed Deep Learning: Illustrations Using First Order Hyperbolic Conservation Law-based Traffic Flow Models. IEEE Open Journal of Intelligent Transportation Systems.
  • Shabab, R., Mustavee, S., Agarwal, S., Zaki, M., & Das, S. (2023). Exploring Dynamic Mode Decomposition for Robust System Identification: Applications to Adaptive Signalised Intersections. Journal of Intelligent Transportation Systems: Technology, Planning, and Operations (Taylor and Francis).
  • Das, S., Mustavee, S., Agarwal, S., Hasan, S. (2023). Koopman-theoretic Modeling of Quasiperiodically Driven Systems: Example of Signalized Traffic Corridor. IEEE Transactions on Systems, Man and Cybernetics: Systems.
  • Krishen, A.S., Berezan, O., Agarwal, S., & Kanchen, S. (2023). Affective commitment recipes for wine clubs: Value goes beyond the vine. Journal of Business Research, 157, 113464 (Elsevier).
  • Huang, A.J. & Agarwal, S. (2022). Physics-informed deep learning for traffic state estimation: illustrations with LWR and CTM Models. IEEE Open Journal of Intelligent Transportation Systems, 3, 503–518.
  • Mustavee, S., Agarwal, S., Enyioha, C., & Das, S. (2022). A linear dynamical perspective on epidemiology: interplay between early COVID-19 outbreak and human mobility. Nonlinear Dynamics, 1–20 (Springer).
  • Krishen, A.S., Berezan, O., Agarwal, S., Kachroo, P., & Raschke, R. (2021). The digital self and virtual satisfaction: A cross-cultural perspective. Journal of Business Research, 124, 254–263 (Elsevier).
  • Muhlmeyer, M., Agarwal, S., & Huang, J. (2020). Modeling Social Contagion and Information Diffusion in Complex Socio-Technical Systems. IEEE Systems Journal, 14(4), 5187–5198.

Older Publications (2014 – 2019)

  • Kachroo, P., Agarwal, S., & Ozbay, K. Aggregated Macroscopic Fundamental Diagram: Basic Theoretical Analysis and Traffic Control. IEEE Transactions on Intelligent Transportation Systems.
  • Agarwal, S. & Kachroo, P. Controllability and Observability Analysis for Intelligent Transportation Systems. Transportation in Developing Economies (Springer), 5(1), 2.
  • Kachroo, P., Agarwal, S., Piccoli, B., & Ozbay, K. Multi-scale Modeling and Control Architecture for V2X Enabled Traffic Streams. IEEE Transactions on Vehicular Technology, 66(6), 4616–4626.
  • Contreras, S., Agarwal, S., & Kachroo, P. Quality of Traffic Observability on Highways With Lagrangian Sensors. IEEE Transactions on Automation Science and Engineering, 15(2), 761–771.
  • Agarwal, S., Kachroo, P., Regentova, E., & Verma, H. Multidimensional Compression of ITS Data Using Wavelet-Based Compression Techniques. IEEE Transactions on Intelligent Transportation Systems, 18(7), 1907–1917.
  • Kachroo, P., Agarwal, S., & Sastry, S. Inverse Problem for Non-viscous Mean Field Control: Example from Traffic. IEEE Transactions on Automatic Control. DOI: 10.1109/TAC.2015.2511929.
  • Agarwal, S., Kachroo, P., Contreras, S., & Sastry, S. Feedback-Coordinated Ramp Control of Consecutive On-Ramps Using Distributed Modeling and Godunov-Based Satisfiable Allocation. IEEE Transactions on Intelligent Transportation Systems, 16(5), 2384–2392.
  • Agarwal, S., Kachroo, P., & Contreras, S. A Dynamic Network Modeling Based Approach for Traffic Observability Problem. IEEE Transactions on Intelligent Transportation Systems, 17(4), 1168–1178.
  • Contreras, S., Kachroo, P., & Agarwal, S. Observability and Sensor Placement Problem on Highway Segments: A Traffic Dynamics-Based Approach. IEEE Transactions on Intelligent Transportation Systems, 17(3), 848–858.
  • Verma, P., Yang, H., Kachroo, P., & Agarwal, S. Modeling and Estimation of Vehicle-miles Traveled (VMT) Tax Rate Using Stochastic Differential Equations. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 46(6), 818–828.
  • Verma, P., Agarwal, S., Kachroo, P., & Krishen, A. Declining transportation funding and need for analytical solutions: dynamics and control of VMT tax. Journal of Marketing Analytics, 5(3–4), 131–140.
  • Kachroo, P., Gupta, S., & Agarwal, S. Optimal Control for Congestion Pricing: Theory, Simulation and Evaluation. IEEE Transactions on Intelligent Transportation Systems, 18(5): 1234–1240.
  • Agarwal, S., Kachroo, P., & Regentova, E. A Hybrid Model Using Logistic Regression and Wavelet Transformation to Detect Traffic Incidents. IATSS Research (Elsevier), 40(1), 56–63.
  • Krishen, A., Kachroo, P., Agarwal, S., Sastry, S., & Wilson, M. Safety culture from an interdisciplinary view: Proposing a hierarchical feedback-based transportation framework. Transportation Journal, 54(4), 516–534.
  • Krishen, A., Agarwal, S., & Kachroo, P. Is Having Accurate Knowledge Necessary for Implementing Safe Practices? A Consumer Folk Theories-of-Mind Perspective on the Impact of Price. European Journal of Marketing, 50(5/6), 1073–1093.
  • Krishen, A., Agarwal, S., Kachroo, P., & Raschke, R. Framing the Value and Valuing the Frame? Algorithms for Child Safety-Set Use. Journal of Business Research, 69(4), 1503–1509.
  • Muhlmeyer, M., Huang, J., & Agarwal, S. Event Triggered Social Media Chatter: A New Modeling Framework. IEEE Transactions on Computational Social Systems, 6(2), 197–207.