Large language models enable increasingly expressive agent-based simulations, but pose methodological challenges regarding behavioral validity. This paper evaluates LLM-driven simulation credibility through a social media test case examining information engagement. Using a Weibo-like environment, the study systematically manipulates information load and descriptive norms while allowing popularity cues to evolve endogenously. The research tests whether simulated user behavior responds systematically to theoretical constructs rather than producing merely plausible outputs. Findings indicate that engagement responds systematically to information load and descriptive norms, with sensitivity to popularity cues varying across contexts. The paper discusses methodological implications for simulation-based communication research, particularly for multi-condition experimental designs involving LLM-driven agents.