Critical Networks and Disinformation

Abstract

The spread of disinformation across both digital and physical networks has created serious challenges for public trust, decision-making, and communication strategies. This thesis builds a stochastic, path-based optimization model that focuses on maximizing the spread of accurate information under real-world uncertainty and budget constraints. The model works by planning a connected route through a network of cities, starting from a source and reaching a target, while dealing with varying travel and campaign costs across different scenarios. An application using a real-world-inspired network shows how changes in budget levels and cost assumptions impact the total number of people influenced and the number of cities reached. Results suggest that taking a conservative approach to cost assumptions allows campaigns to cover more ground and reach more people, while higher-cost or uncertain scenarios limit both reach and attendance. This work offers a flexible framework for organizing strategic communication efforts and opens the door for future research on evaluating efficiency and adapting strategies as conditions change.