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arXiv2026

Robust Opinion Maximization

Opinion-formation models are fundamental for analyzing social behavior and understanding social dynamics. The Friedkin-Johnsen (FJ) model is the most commonly used model in this line of research. In FJ, the expressed opinion of an individual evolves over time as the combination of the opinions of their peers and a fixed inner opinion, weighted by their stubbornness. A problem that has received considerable attention is the problem of Opinion Maximization, where the goal is to adjust the parameters of the FJ model, such as stubbornness, or inner opinions, to maximize the average expressed opinion, i.e., the public opinion. Most existing work assumes full knowledge of the parameters of the model, which is unrealistic. In this paper, we introduce the problem of Robust Opinion Maximization, where we assume uncertainty for the inner opinions of the nodes, and we maximize the public opinion under worst-case assumptions on this uncertainty. Using tools from Robust Optimization, we formulate our problem as a max-min problem, and we develop an optimal two-step solution that is both highly intuitive and efficient. Experiments on several real-world networks demonstrate that our approach can achieve robust solutions for million-node graphs in minutes, and reveal that our robust strategies can significantly mitigate the impact of noise compared to standard baselines.

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arXiv2026

Not Everyone Has a Voice, or an Ear, on Social Media: The Poster-Observer Model of Opinion Dynamics

Classic modeling of opinion dynamics via the Friedkin-Johnsen (FJ) model relies on the unrealistic assumption of synchronous communication among network users. Social media, however, exhibit extreme participation asymmetries: a vast majority of users silently observe, while a tiny minority creates and broadcasts most of the content. To capture this reality, we introduce the Poster-Observer FJ (PO-FJ) model, which decouples user communication into distinct posting and observing behaviors. This formulation fundamentally alters network dynamics by preventing convergence to a static equilibrium, driving opinions into perpetual oscillations. We establish closed-form expressions for these new dynamics and generalize classic metrics of public opinion, polarization, and disagreement under the PO-FJ model. By leveraging the novel Poster-Observer dynamics, we introduce a realistic and scalable intervention algorithm for conflict mitigation. Through experiments on synthetic and real-world networks, we demonstrate the efficacy and efficiency of our approach in reducing social-network conflict.

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