This paper studies the use of intuitive prompting to improve the fidelity of language models (LLMs) simulating social media reactions. The authors found that instructing LLMs to respond intuitively and immediately resulted in higher fidelity and better performance on unfamiliar content, suggesting potential applications for general-purpose simulated users.