Online Minimization of Polarization and Disagreement via Low-Rank Matrix Bandits

We study the problem of minimizing polarization and disagreement in the Friedkin-Johnsen opinion dynamics model under incomplete information. Unlike prior work that assumes a static setting with full knowledge of agents' innate opinions, we address the more realistic online setting where innate opinions are unknown and must be learned through sequential observations. This novel setting, which naturally mirrors periodic interventions on social media platforms, is formulated as a regret minimization problem, establishing a key connection between algorithmic interventions on social media platforms and the theory of multi-armed bandits. In our formulation, a learner observes only a scalar feedback of the overall polarization and disagreement after an intervention. For this novel bandit problem, we propose a two-stage algorithm based on low-rank matrix bandits. The algorithm first performs subspace estimation to identify an underlying low-dimensional structure, and then employs a linear bandit algorithm within the compact dimensional representation derived from the estimated subspace. We show that our algorithm achieves the cumulative regret of $\widetilde{\mathcal{O}}\big(\max(\tfrac{1}{\kappa},\sqrt{|V|})\sqrt{|V|T}\big)$ over time horizon $T$, where $V$ is the set of agents and $\kappa$ is a parameter dependent on the diversity of interventions. Empirical results validate that our algorithm significantly outperforms a linear bandit baseline in terms of both cumulative regret and running time.