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数据分析与知识发现  2022, Vol. 6 Issue (8): 97-109     https://doi.org/10.11925/infotech.2096-3467.2021.1266
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于动态演化的大学生网络舆情预警模型研究*
李川1,2,朱学芳1(),富子元1
1南京大学信息管理学院 南京 210023
2安徽财经大学管理科学与工程学院 蚌埠 233030
Early-warning Model for Undergraduate Public Opinion with Dynamic Evolution
Li Chuan1,2,Zhu Xuefang1(),Fu Ziyuan1
1School of Information Management, Nanjing University, Nanjing 210023, China
2School of Management Science and Engineering, Anhui University of Finance and Economics, Bengbu 233030, China
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摘要 

【目的】 研究社会演化分析和系统动力学方法在大学生舆情预警及策略研判的应用。【方法】 基于用户行为理论进行舆情系统分析,利用系统动力学仿真分析舆情要素、官方机构、社会媒体、大学生及互联网环境对舆情的作用机理,构建大学生网络舆情预警系统动力学模型。【结果】 通过三组仿真实验对模型假设进行分析判断,验证了舆情控制要素的影响范围,证伪了公信力的控制效应,较其他模糊认知模型,ACR提升1.4%,CPT降低50%。【局限】 由于关联因素的提取依赖研究对象和环境演化,模型需要持续优化,按照子系统划分组织专业团队检验调整。【结论】 运用仿真拟合实验法初步得出了预警机制、官方公信力、事件属性等控制要素对舆情的影响,并以此提出一种面向大学生舆情问题的信息分析方法。

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李川
朱学芳
富子元
关键词 大学生舆情预警分析系统动力学仿真实验    
Abstract

[Objective] This paper tries to examine the social evolution analysis and System Dynamics for early warning strategies of undergraduates’ public opinion administration. [Methods] We conducted system analysis for public opinion based on user behavior theory. We analyzed the mechanism among undergraduates, official institutions, Internet environment, public opinion elements and social media with System Dynamics (SD). Finally, we built a new SD model for the early warning system of public opinion. [Results] We evaluated our model with three simulation experiments. The influence range of control elements was verified, while the control effect of credibility was falsified. Compared with other fuzzy cognitive models, our algorithm’s ACR increased by 1.4% and the CPT reduced by 50%. [Limitations] Extracting related factors depends on the research object and environmental evolution, and our model needs to be continuously optimized in the future. [Conclusions] The proposed model creates an early warning mechanism for public opinion from the undergraduate communities.

Key wordsPublic Opinion for Undergraduates    Forewarning Analysis    System Dynamics    Simulation Experiment
收稿日期: 2021-11-05      出版日期: 2021-12-20
ZTFLH:  G202  
基金资助:*安徽省高校科学研究重点项目(SK2021A0252);安徽财经大学科学研究项目的研究成果之一(ACKYC21057)
通讯作者: 朱学芳,ORCID:0000-0002-8244-5999     E-mail: xfzhu@nju.edu.cn
引用本文:   
李川, 朱学芳, 富子元. 基于动态演化的大学生网络舆情预警模型研究*[J]. 数据分析与知识发现, 2022, 6(8): 97-109.
Li Chuan, Zhu Xuefang, Fu Ziyuan. Early-warning Model for Undergraduate Public Opinion with Dynamic Evolution. Data Analysis and Knowledge Discovery, 2022, 6(8): 97-109.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2021.1266      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2022/V6/I8/97
Fig.1  大学生舆情的运作机理
Fig.2  官方作用子系统示意图
Fig.3  舆情耗散子系统示意图
Fig.4  虚拟社区子系统示意图
Fig.5  社会媒体子系统示意图
Fig.6  大学生舆情预警SD模型
时段 病例
新增数
主题数 信息发布数量 浏览量
社会媒体 官方机构
6.11-6.15 135 305 798 233 3.13×106
6.16-6.20 155 572 122 414 7.26×106
6.21-6.25 95 315 434 635 2.74×106
6.26-6.30 82 180 573 758 1.53×106
Table 1  2020年6月中下旬关于“疫情封校”的舆情统计
Fig.7  大学生舆情信息发布情况对比
参数 SD 模型 多级模糊认知模型
精确度 0.847 0.835
响应时间 83 s 167 s
定义域 大学生舆情 多场景网络信息
Table 2  SD模型与多级模糊认知模型仿真效果
Fig.8  大学生舆情影响力变化趋势
Fig.9  预警机制对舆情的影响
Fig.10  官方公信力对舆情的影响
Fig.11  事件属性对舆情的影响
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