Towards Consensus: Reducing Polarization by Perturbing Social Networks

Abstract

This paper studies how a centralized planner can modify the structure of a social or information network to reduce polarization. First, polarization is found to be highly dependent on degree and structural properties of the network. We then formulate the planner's problem under full information, and motivate disagreement-seeking and coordinate descent heuristics. A novel setting for the planner in which the population's innate opinions are adversarially chosen is introduced, and shown to be equivalent to maximization of the Laplacian's spectral gap. We prove bounds for the effectiveness of a strategy that adds edges between vertices on opposite sides of the cut induced by the spectral gap's eigenvector. Finally, these strategies are evaluated on six real-world and synthetic networks. In several networks, we find that polarization can be significantly reduced through the addition of a small number of edges.

Social Depolarization and Diversity of Opinions—Unified ABM Framework

“…There is no room to review all the proposed strategies and analyses of their effectiveness. A sample of the recent literature is given by: [ 24 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 ]. The evidence is mixed, in that some depolarizing methods work in some environments, but fail in others.…”

“…Due to the social importance of the persistent conflicts and increasing divide spanning all levels of democratic societies on many issues, the current generation of opinion models often focus on the issue [ 103 ]. However, as already noted, the number of models devoted to reversing the polarization trends remains limited (see, for example, [ 34 ]).…”