Tracking the spread of climate change skepticism on X with simulations and deep learning

Climate change continues to be a global challenge that requires urgent action. However, the ongoing presence of climate skepticism undermines society's ability to confront this important challenge. Understanding the mechanisms driving the spread of climate skepticism might give policymakers additional tools to combat climate change. Here, we propose a methodological approach that combines computational simulation (in the form of an agent-based model representing online X communication) with simulation-based inference using amortized deep neural networks. Our approach allows us to infer the relative importance of a variety of different learning strategies that can contribute to the spread of climate skepticism and support.