Adaptive link dynamics drive online hate networks and their mainstream influence

TL;DR

This paper uncovers how adaptive online hate networks evolve and influence mainstream platforms, providing a scientific framework with dynamical equations and predictions to better understand and mitigate their impact.

Contribution

It introduces a novel dynamical model of adaptive hate networks, revealing their growth mechanisms and influence on mainstream platforms, supported by quantitative predictions.

Findings

Hate networks connect across platforms, strengthening over time.

A tipping point predicts surges in hate content transmission.

Mitigation impacts can be quantitatively forecasted.

Abstract

Online hate is dynamic, adaptive -- and is now surging armed with AI/GPT tools. Its consequences include personal traumas, child sex abuse and violent mass attacks. Overcoming it will require knowing how it operates at scale. Here we present this missing science and show that it contradicts current thinking. Waves of adaptive links connect the hate user base over time across a sea of smaller platforms, allowing hate networks to steadily strengthen, bypass mitigations, and increase their direct influence on the massive neighboring mainstream. The data suggests 1 in 10 of the global population have recently been exposed, including children. We provide governing dynamical equations derived from first principles. A tipping-point condition predicts more frequent future surges in content transmission. Using the U.S. Capitol attack and a 2023 mass shooting as illustrations, we show our findings provide abiding insights and quantitative predictions down to the hourly scale. The expected impacts of proposed mitigations can now be reliably predicted for the first time.