面向網絡輿情演變過程中政府應急響應需求,本文基于SOAR(State,Operator and Result)模型,將網民作為智能體Agent,將網絡輿情中網民群體行為轉變過程看作相應輿情問題空間中狀態隨時間的連續轉換過程,設計突發事件中網民群體負面情感SOAR Agent模型,包括網民Agent的工作記憶、長期記憶、決策過程、學習機制,構建網民群體行為轉換規則庫和相應算法。在此基礎上,設計仿真實驗,結合典型網絡輿情事件案例,對政府不同應急措施下微博用戶群體行為演變過程進行建模。實證結果表明,基于網民群體負面情感的建模,可以分析和預測在不同網絡輿情階段、不同政府應急響應措施下網民群體的行為決策規律。

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.