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作 者:杨洋洋 Yang Yangyang(School of Economics&Management,Zhengzhou University of Light Industry,Zhengzhou 450001)
机构地区:[1]郑州轻工业大学经济与管理学院,郑州450001
出 处:《情报杂志》2024年第1期167-175,105,共10页Journal of Intelligence
基 金:河南省哲学社会科学规划项目“重大疫情中网络舆情触发机制与精准引导策略研究”(编号:2022CXW024);郑州轻工业大学博士科研基金资助“面向重大突发事件的网络舆情传播机制与管控策略研究”(编号:2022BSJJSK03);郑州轻工业大学“党的二十大精神研究”专项课题“企业突发舆情事件主题图谱与生成机制研究”研究成果。
摘 要:[研究目的]面对复杂的网络环境,探讨影响舆论聚合的关键要素,既有助于舆论信息的高效传播,又能够提高政府舆论调控的速度和精度,具有重要的现实意义。[研究方法]采用VAR和fsQCA相融合的方法,选取28个视频平台负面事件作为研究对象,运用VAR模型验证了信息粒度、公众行为与舆论聚合之间的两两变量因果关系,在此基础上,运用fsQCA方法探究了信息权威性、信息覆盖度、信息报道率、公众关注度、公众活跃度、公众参与期6个前因条件组合对舆论聚合的影响机制。[研究结论]研究发现,信息粒度、公众行为与舆论聚合之间存在显著的两两变量因果关系,挖掘出了8种导致高舆论聚合的条件组态,信息报道率、公众关注度、公众活跃度为核心条件,提炼归纳了公众行为驱动型、信息粒度驱动型、信息-公众驱动型的三种高舆论聚合的理论模式。[Research purpose]Facing the complex network environment,this paper discusses the key factors that affect the aggregation of public opinion.It not only contributes to the efficient dissemination of public opinion information,but also can improve the speed and accuracy of government public opinion regulation.It has important practical significance.[Research method]Using VAR and fsQCA fusion method,this paper selects 28 video platform negative events as the research object.This paper uses VAR model to verify the two-variable variable causality between information granularity,public behavior and consensus aggregation.On this basis,this paper uses the fsQCA method to explore the influence mechanism of the combination of six antecedents on the aggregation of public opinion:information authority,information coverage,information coverage,public concern,public activity,and public participation period.[Research conclusion]It is found that there is a significant two-variable variable causal relationship between information granularity,public behavior and consensus aggregation.In this paper,eight kinds of conditional configurations that lead to the convergence of high opinion are discovered.Information reporting rate,public concern and public activity are the core conditions.This paper summarizes three theoretical models of high opinion aggregation:public behavior driven,information granularity driven,and information public driven.
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