Optimizing Social Network Interventions via Hypergradient-Based Recommender System Design

Although social networks have expanded the range of ideas and information accessible to users, they are also criticized for amplifying the polarization of user opinions. Given the inherent complexity of these phenomena, existing approaches to counteract these effects typically rely on handcrafted algorithms and heuristics. We propose an elegant solution: we act on the network weights that model user interactions on social networks (e.g., ranking of users’ shared content in feeds), to optimize a performance metric (e.g., minimize polarization), while users’ opinions follow the classical Friedkin-Johnsen model. Our formulation gives rise to a challenging, large-scale optimization problem with non-convex constraints, for which we develop a gradient-based algorithm. Our scheme is simple, scalable, and versatile, as it can readily integrate different, potentially non-convex, objectives. We demonstrate its merit by: (i) rapidly solving complex social network intervention problems with 4.8 million variables based on the Reddit, LiveJournal, and DBLP datasets; (ii) outperforming competing approaches in terms of both computation time and disagreement reduction.

Social media platforms have made it easier to share ideas and connect with others, but they can also contribute to increasing polarization by reinforcing existing beliefs and limiting exposure to diverse viewpoints. Addressing this challenge is complex, and most current solutions rely on manually crafted rules or heuristics. In our work, we present a flexible approach that adjusts the interactions between users to reduce a wide range of objectives, such as polarization or disagreement. Our method is scalable, capable of handling networks with millions of users, and adaptable to a range of objectives beyond polarization. When evaluated on real-world data, our approach significantly outperforms existing techniques, both in reducing disagreement and in computational efficiency.