面向网络舆情演变过程中政府应急响应需求，本文基于SOAR （State，Operator and Result）模型，将网民作为智能体Agent，将网络舆情中网民群体行为转变过程看作相应舆情问题空间中状态随时间的连续转换过程，设计突发事件中网民群体负面情感SOAR Agent模型，包括网民Agent的工作记忆、长期记忆、决策过程、学习机制，构建网民群体行为转换规则库和相应算法。在此基础上，设计仿真实验，结合典型网络舆情事件案例，对政府不同应急措施下微博用户群体行为演变过程进行建模。实证结果表明，基于网民群体负面情感的建模，可以分析和预测在不同网络舆情阶段、不同政府应急响应措施下网民群体的行为决策规律。
To meet the demands of government crisis response in the evolution of network public opinion, an internet users' negative emotion model based on SOAR(State,Operator and Result) model is proposed, where internet users is treated as Agent, behavior transformation process of internet user group in network public opinion is considered as continuous transformation process of state with time in the corresponding public opinion space in this paper. Then, working memory, long-term memory, decision-making process, learning mechanism of Agent are designed, internet user behavior transformation rule repository is constructed, and SOAR Agent model to reveal behavior transformation rules of internet user group is developed ultimately. Moreover, an simulation experiment is designed, typical public opinion events are seleted as samples, group behavior transformation process of Weibo users under the influence of different government emergency measures is conducted, and the SOAR Agent model of internet users' negative emotion is validated. As result of this research, the collective behavior transformation rules of behavior transfer of internet users in the different environments could be analyzed and predicted based on the modelling of internet users' negative emotion, such as different period of internet public opinion, different government crises response strategy.
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