Breaking the Illusion of Artificial Consensus: Clone-Robust Weighting in Arbitrary Metric Spaces

Abstract

Social media, pseudonymous identities and generative AI have made online platforms highly vulnerable to Coordinated Inauthentic Behavior: by generating large numbers of similar posts, influence campaigns create an illusion of widespread support, exploiting the tendency of users and algorithms alike to mistake frequency for consensus. Clone-robust weighting functions address this problem by assigning influence in a way that is insensitive to near-duplication, as measured by a metric, while remaining symmetric, continuous and local. While prior constructions were restricted to Euclidean spaces, we present a computationally efficient framework for arbitrary metric spaces, based on neighborhood graphs and graph weighting functions satisfying a simple locality condition. As a consequence, the visibility an attacker gains from near-duplicates is bounded independently of their number. We further argue that, in democratically sensitive settings such as news feeds, such guarantees must come with explainability, and introduce sharing coefficients that quantify how influence is transferred between elements of prior volumetric constructions. In the graph setting, we show that sharing measures based on the deletion of vertices or edges are incompatible with locality, and instead define sharing through the addition of clones. Finally, we characterize the resulting interpretability properties and construct clone-robust graph weighting functions satisfying them.

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