突发公共卫生事件网络谣言网格化预警模型研究

doi:10.16381/j.cnki.issn1003-207x.2021.1501

Abstract

Abstract:

In order to improve the problem of low efficiency and insufficient reliability of online rumors early warning， a grid-based early warning model for online rumors of public health emergencies is established. Firstly， the depth of confusion of online rumors to individual audience is identified longitudinally by quantifying the basic characteristics of online rumors based on the grey-weighted correlation analysis. Secondly， the spread of online rumors to audience groups is predicted horizontally combined the dynamic game of incomplete information with simulation. Finally， the assessment results of both vertical and horizontal dimensions are integrated to define the overall social influence of online rumors， and a visual four-level early warning classification for mild， warning， severe，and danger is carried out， and four emergency management and control strategies of delay， suppression， confronation and suppression are correspondingly proposed. The case of “Shuanghuanglian” is taken as an example to verify the effectiveness of the online rumors grid-based early warning model and its choke tactics. In terms of theoretical research value， it is the first time that the dual perspectives of “identification-warning” and “dissemination-warning” are combined to predict the early-warning level of the overall social influence of online rumors. Based on the interaction between information and audience， the information vagueness of online rumor is estimated more comprehensively， and both the individual cognitive judgment of the audience and the game psychology of the audience group are considered in the evaluation of the spread of online rumors. At the same time， the whole process of this study relies on big data， artificial intelligence and other automatic technologies to improve the objectivity of the evaluation of social influence of online rumors. On the basis of visualizing the four-level warning （mild level， warning level， severe level and danger level） of the overall social influence of online rumors， four emergency management and control strategies are proposed respectively， which promoted the study of online rumors governance.

Key words: public health emergencies, online rumors, early warning model, grey-weighted correlation analysis, dynamic game with incomplete information

CLC Number:

G202

Guirong Zhang,Zhixiang Dong,Ting Xia. Study on the Grid-based Early Warning Model for Online Rumors of Public Health Emergencies[J]. Chinese Journal of Management Science, 2024, 32(2): 298-306.

Figures/Tables 9

References 18

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一级特征	二级特征	量化赋值
用户特征	微博发布主体属性	个人赋值1，新闻媒体赋值2，政府部门赋值3
	粉丝数及关注数	不足1千人赋值1，1千-2千人赋值2，2千-5千人赋值3，5千-1万赋值4，超过1万人赋值5
	是否为验证用户	非验证用户赋值1，验证用户赋值2
结构特征	微博的叙事结构	快转赋值1，转发评论赋值2
	微博内容总长度	评论字数在1-28区间赋值1，评论字数在29-56区间赋值2，评论字数在57-84区间赋值3，评论字数在85-112区间赋值4，评论字数在113-140区间赋值5
	微博的承载形式	内容为纯文字1，包含涉事图片赋值2，包含涉事视频赋值3
	微博的原创性讨论数	不足1千人赋值1，1千-5千人赋值2，5千-2万人赋值3，2万-10万人赋值4，超过10万人赋值5
传播特征	微博的转发次数	不足1千人赋值1，1千-5千人赋值2，5千-2万人赋值3，2万-10万人赋值4，超过10万人赋值5
	微博的评论次数	不足1千人赋值1，1千-5千人赋值2，5千-2万人赋值3，2万-10万人赋值4，超过10万人赋值5
	微博的多次传播	不含@符号赋值1，含有@符号赋值2
情感特征	公众情绪分布	各情绪分布划分情况
情感特征	公众情绪强度	记录各类情绪所占比例大小

	用户特征	结构特征	传播特征	情感特征	权重
用户特征	1	1/2	5	5	0.3953
结构特征	2	1	4	1/9	0.1936
传播特征	1/7	1/4	1	1/2	0.0560
情感特征	1/5	9	2	1	0.3551

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Study on the Grid-based Early Warning Model for Online Rumors of Public Health Emergencies

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