图书情报工作 ›› 2019, Vol. 63 ›› Issue (20): 6-16.DOI: 10.13266/j.issn.0252-3116.2019.20.001

• 专题:网络舆情监控与追踪的理论和技术研究 • 上一篇    下一篇

多媒体网络舆情危机响应机理及风险分型研究

周昕, 李瑞, 黄微   

  1. 吉林大学管理学院 长春 130022
  • 收稿日期:2019-03-08 修回日期:2019-06-04 出版日期:2019-10-20 发布日期:2019-10-20
  • 作者简介:周昕(ORCID:0000-0002-8601-6171),讲师,博士,E-mail:gloria-asi@163.com;李瑞(ORCID:0000-0002-1080-0912),博士研究生;黄微(ORCID:0000-0003-0448-9563),教授,博士生导师。
  • 基金资助:
    本文系国家自然科学基金面上项目"大数据环境下多媒体网络舆情信息的语义识别与危机响应研究"(项目编号:71473101)研究成果之一。

Research on Crisis Response Mechanism and Risk Classification of Multimedia Network Public Opinion

Zhou Xin, Li Rui, Huang Wei   

  1. School of Management, Jilin University, Changchun 130022
  • Received:2019-03-08 Revised:2019-06-04 Online:2019-10-20 Published:2019-10-20

摘要: [目的/意义]针对当前我国多媒体网络舆情响应问题,梳理并提出以危机风险分型为基础的政府组织响应路径整合匹配、响应工作流程模型构建机理,以期为管理决策者提升资源整合能力、网络舆情危机精准响应效力提供参考。[方法/过程]对大数据环境下网络舆情危机动力要素的作用进行分析,提取出网络舆情危机风险分型的基础系数,并以多元结构网络舆情信息的多媒体传播路径为视角提取网络舆情危机风险分型叠加系数,再逐一提取各风险分型下网络舆情危机响应的工作要点。[结果/结论]根据主体结构要素、媒体效力要素、客体属性要素危机作用形态的排序组合,在其关系节点上建立多媒体网络舆情危机等级基数。根据本体成分分化后对舆情危机的不同影响效果,建立多媒体网络舆情危机加成系数。建立多媒体网络舆情危机风险分型模型,将舆情危机风险解构为等级系数和加成系数。从而更为准确地描述舆情危机的表征,有利于判断舆情危机的未来发展态势,提高与既往舆情危机案例匹配的速度与精准度。

关键词: 多媒体网络舆情, 网络舆情危机, 舆情危机响应, 危机风险分型

Abstract: [Purpose/significance] Aiming at the current public opinion response problem of multimedia network in China, this paper combs and proposes the mechanism of government organization response path integration matching and response workflow model based on crisis risk classification. In order to improve the resource integration ability for management decision-makers, the network public opinion crisis provides a reference for the accurate response effectiveness.[Method/process] This paper analyzes the role of the dynamic factors of network public opinion crisis in big data environment, extracts the basic coefficients of network public opinion crisis risk classification, and extracts the network public opinion crisis risk classification superposition coefficient from the perspective of the multi-structure of network public opinion information carried by multimedia technology. Then the paper analyzes the work points of the network public opinion crisis response under each risk classification one by one.[Result/conclusion] According to the ordering combination of the main structural elements, the media effectiveness factors and the object attribute elements, the credit base level of the multimedia network is established on the relationship nodes. According to the different influence effects of the ontology component on the public opinion crisis, the public opinion crisis of the big data multimedia network is established. Establishing a multimedia network public opinion crisis risk classification model, deconstructing the public opinion crisis risk into a ranking coefficient and an addition coefficient, and a more accurate description of the lyric crisis is conducive to judging the future development of the grievance crisis and improving the speed and accuracy of matching with the previous grievance crisis case.

Key words: multimedia network public opinion, network public opinion crisis, public opinion crisis response, crisis risk classification

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