突发事件情境下社会共识形成机理  

Social Consensus Building Mechanisms in the Context of Public Emergencies

作　　者：安璐[1,2] 郑雅静 An Lu;Zheng Yajing(Center for Studies of Information Resources,Wuhan University,Wuhan 430072,China;School of Information Management,Wuhan University,Wuhan 430072,China)

机构地区：[1]武汉大学信息资源研究中心,武汉430072 [2]武汉大学信息管理学院,武汉430072

出　　处：《数据分析与知识发现》2024年第12期1-17,共17页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目(项目编号:72174153,71921002)的研究成果之一

摘　　要：【目的】探究突发事件情境下社会共识形成机理,提出识别和度量共识的方法,揭示影响共识形成的重要因素,为相关部门制定有效的信息传播策略、引导舆论演化提供理论与方法支撑。【方法】以某市烧烤店事件的微博数据为数据源,结合主题模型、情感分析和三元组抽取等方法挖掘用户观点,基于观点一致性和情感一致性计算个体间共识度;采用信息生态理论,从信息人、信息、信息环境等维度构造特征变量,构建共识度预测模型;比较4个机器学习模型性能,使用SHAP对最优模型进行解释。【结果】CatBoostRegressor模型的MSE值(1 176.955 0)和R2值(0.675 3)优于其他三个模型;特征重要性排名前五的因素中,受高等教育人群占比、年龄差距、观点坚定者占比与群体共识度呈显著负相关,社交网络结构相似度与群体共识度呈显著正相关,在不同话题上各特征变量的影响方式有所不同。【局限】仅关注不同群体内的共识,未探究不同群体间的观点演化以及共识形成机制。【结论】本文方法能够揭示影响社会共识形成的关键因素。[Objective]This study aims to explore the mechanism of social consensus formation in the context of public emergencies.It proposed the methods for identifying and measuring consensus and identifies important factors that influence consensus formation,providing theoretical and methodological support for relevant departments to formulate effective information dissemination strategies and guide the evolution of public opinion.[Methods]This study takes the microblogging data of the barbecue restaurant incident in a city as a data source,combines the topic model,sentiment analysis and triplet extraction to explore users’opinions.The degree of consensus among individuals is calculated based on opinion consistency and emotional consistency.Using the information ecology theory,the characteristic variables are constructed from the dimensions of information people,information,and information environment.The consensus degree prediction model is established.The performance of the four machine learning models is compared.The SHapley Additive ExPlanations(SHAP)technique is used to explain the best model.[Results]As a result,the MSE value(1176.9550)and the R-squared value(0.6753)of the CatBoostRegressor model were found to be superior to the other three models.The top five factors in the feature importance ranking show that the proportion of people with higher education,the age gap,and the number of people with firm views are significantly negatively correlated with the degree of group consensus.Similarity of social network structure is significantly positively correlated with the degree of group consensus.The impact of the feature variables varies according to the topic.[Limitations]Social consensus includes intragroup consensus and intergroup consensus.This article focuses only on consensus within different groups,and further research on the evolution of viewpoints and consensus formation mechanisms between different groups can be conducted in the future.[Conclusions]This article proposes a method for identifying and measur

关 键 词：社会共识 网络用户 社交媒体 观点识别 突发事件 

分 类 号：D63[政治法律—政治学] C912.63[政治法律—中外政治制度] G206[经济管理]