Fairness in Opinion-Formation Dynamics

摘要

Opinion formation models are widely used to study social and behavioral processes on online social networks, yet their fairness remains largely unexplored. We study this novel problem in the context of the Friedkin–Johnsen model, a well-established framework for opinion dynamics. In this model, the expressed opinion of an individual evolves by combining peer opinions with a fixed inner opinion weighted by their stubbornness. We define a node's influence as the weight its inner opinion contributes to the public opinion. Given different groups of nodes, we require that influence is distributed fairly across groups. To achieve this, we design minimal interventions that adjust stubbornness, making individuals more receptive to others or more anchored to their own views. We derive closed-form expressions for how changes in the stubbornness of a single node affect influence and leverage them to develop efficient algorithms. Experiments on synthetic and real-world networks provide insights into the role of stubbornness in fairness and demonstrate the effectiveness and efficiency of our methods.