Modeling social media public opinion evolution is essential for governance decision-making. Traditional epidemic models and rule-based agent-based models (ABMs) fail to capture the cognitive processes and adaptive behaviors of real users. Recent large language model (LLM)-based social simulations can reproduce group-level phenomena like polarization and conformity, yet remain unable to recreate the irrational interactions and multi-phase dynamics of real public opinion events. We present POSIM (Public Opinion Simulator), a multi-agent simulation framework for social media public opinion evolution and governance. POSIM integrates LLM-driven agents with a Belief--Desire--Intention (BDI) cognitive architecture that accounts for irrational factors, places them in a virtual social media environment with social networks and recommendation mechanisms, and drives temporal dynamics through a Hawkes point process engine that captures the co-evolution of agents and the environment across event phases. To validate the framework, we collect real-world public opinion datasets from the Weibo platform covering the full interaction chain of users. Experiments show that POSIM successfully reproduces key characteristics of public opinion evolution from individual mechanisms to collective phenomena, and its effectiveness is further supported by multiple statistical metrics. Building on POSIM, governance-oriented guidance and intervention experiments uncover a counterintuitive empathy paradox: empathetic guidance deepens negative sentiment instead of easing it under certain conditions, offering new insights for governance strategy design. These results demonstrate that the proposed framework can fully serve as a computational experimentation platform for proactive strategy evaluation and evidence-based governance. All source code is available at https://github.com/DeepCogLab/posim/.

翻译：对社交媒体舆情演化进行建模对于治理决策至关重要。传统的传染病模型和基于规则的智能体模型（ABMs）无法捕捉真实用户的认知过程与适应性行为。近期基于大语言模型的社交模拟虽能复现极化、从众等群体层面现象，但仍难以再现真实舆情事件中的非理性交互与多阶段动力学特征。本文提出POSIM（舆情模拟器），一个用于社交媒体舆情演化与治理的多智能体仿真框架。POSIM将大语言模型驱动的智能体与融入非理性因素的信念-愿望-意图认知架构相结合，将其置于具有社交网络和推荐机制的虚拟社交媒体环境中，并通过霍克斯点过程引擎驱动时间动态，捕捉智能体与环境在事件阶段间的协同演化。为验证该框架，我们从微博平台采集涵盖用户完整交互链的真实舆情数据集。实验表明，POSIM成功复现了从个体机制到集体现象的舆情演化关键特征，多项统计指标进一步支撑其有效性。基于POSIM的治理导向引导与干预实验揭示了一个反直觉的共情悖论：在特定条件下，共情式引导非但未能缓和负面情绪，反而加深了消极倾向，为治理策略设计提供了新见解。这些结果表明，所提框架可完全用作前瞻性策略评估与循证治理的计算实验平台。所有源代码已开源至https://github.com/DeepCogLab/posim/。