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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (2/3): 110-121    DOI: 10.11925/infotech.2096-3467.2019.0636
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Tracking Online Public Opinion Based on System Dynamics: Case Study of “Xiangshui Explosion Accident”
Deng Jiangao1,Zhang Xuan1,Fu Zhu1,2(),Wei Qingming1
1School of Enterprise Administration, Hohai University, Changzhou 213022, China
2School of Economic and Management, Jiangsu University of Science and Technology, Zhenjiang 212003, China
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Abstract  

[Objective] This paper explores the dissemination laws of online public opinion during emergencies (OPOE), aiming to help governments guide and regulate such information.[Methods] First, we used “Xiangshui Explosion Accident in Jiangsu Province” as an example and introduced unique variables for this type of events. Then, we constructed a system dynamics model for OPOE. Third, we simulated and analyzed the proposed model with Vensim software. Finally, we adopted the government-related variables as control variables to discuss the impact of government behavior on online public opinion.[Results] For the simulation experiment, the MAPE values of the online posts and news were 18% and 27%. Thus, the simulation model is feasible and could effectively describe the developing trends of online public opinions. More importantly, the government reactions also posed significant effects to the dissemination of public opinions.[Limitations] Some of our data were from questionnaires and expert scoring, which might be biased.[Conclusions] The OPOE generally rises rapidly to the peak and then slowly declines. The government response time, level of reactions and transparency of official news posed positive, negative and negative effects to evolving of public opinions.

Key wordsEmergency      Online Public Opinion      Chemical Pollution      System Dynamics     
Received: 10 June 2019      Published: 26 April 2020
ZTFLH:  G350  
Corresponding Authors: Zhu Fu     E-mail: fuzhu886@163.com

Cite this article:

Deng Jiangao,Zhang Xuan,Fu Zhu,Wei Qingming. Tracking Online Public Opinion Based on System Dynamics: Case Study of “Xiangshui Explosion Accident”. Data Analysis and Knowledge Discovery, 2020, 4(2/3): 110-121.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2019.0636     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2020/V4/I2/3/110

Formation and Development of Online Public Opinion
Modeling Process of System Dynamics
Causality of Netizen Subsystem
Causality of Network Media Subsystem
Causality of Government Subsystem
Causality of Overall System
Stock Flow of Overall System
子系统 序号 变量名称 公式/值 备注
网民
子系统
1 网民微博发帖量 INTEG(微博发布增加量-微博发布减少量,160) 网民一天内在微博上的总发帖量,公式由系统自定义。
2 网民微博发布增加量 网民微博发帖量×EXP(-网民关注度) 有关事件的所有微博在一天内的增加量。
3 网民微博发布减少量 微博沉寂系数×网民微博发帖量 网民微博发帖量降低的速率和网民微博发帖量相乘即为微博发布减少量。
4 网民微博沉寂系数[20] 0.23 由于网民关注度下降、时间流逝等种种原因,使得水污染事件不再受到关注。沉寂系数是指网民微博发帖量降低的速率。
5 网民参与度[21] 100-34.8×EXP(-0.000 083 88×网民微博发帖量) 模型假设网民微博发帖量能够代表网民参与度,其中权重值参考文献[21]。
6 网络舆情热度[15] 0.5036×网民参与度+0.3067×网媒参与度-0.1897×网民对政府满意度 网络舆情热度受到这三个因素的直接作用,三个因素的权重由专家打分法得到,最终权重结果采用各个专家平均值。
7 网民关注度 水体污染程度×(0.10×政府关注度+0.40×网媒参与度+0.25×网络舆情热度-0.25×网民对政府满意度) 网民关注度受到这5个因素的直接作用,其中的权重值由专家打分法得到,最终权重结果采用各个专家平均值。
网媒
子系统
8 网媒新闻发布量 INTEG(网媒新闻增加量-网媒新闻减少量,8) 网络媒体一天内的新闻发布量,公式由系统自定义。
9 网媒新闻沉寂系数[20] 0.28 网媒新闻发布量降低的速率。由于网民关注度下降、时间流逝等种种原因,使得水污染事件不再受到关注。
10 网媒新闻增加量 网媒新闻发布量×EXP(-网民关注度) 事件相关所有网媒新闻在一天内的增加量。
11 网媒新闻减少量 网媒新闻发布量×网媒新闻沉寂系数 用网媒新闻发布量降低的速率和网络网媒新闻发布量相乘即为网媒新闻减少量。
12 网媒参与度[21] 100-95.3×EXP(-0.000 422×网媒新闻发布量) 模型假设网媒新闻发布量能够代表网媒参与度,其中权重值参考文献[21]。
政府
子系统
13 新闻发布量 INTEG(新闻发布增加量-新闻发布减少量,初始值为4) 政府及官方部门在一天内的新闻发布量,公式由系统自定义。
14 新闻发布增加量 新闻发布量×(0.2+EXP(-政府关注度)) 有关事件的所有官方新闻在一天内的增加量。
15 新闻发布减少量 新闻发布量×新闻沉寂系数 用新闻发布量降低的速率和新闻量相乘即为新闻发布减少量。
16 新闻沉寂系数[20] 0.30 新闻发布量降低的速率。由于网民关注度下降、时间流逝等种种原因,使得水污染事件不再受到关注。
17 政府关注度 网络舆情热度×水体污染程度 政府关注度与网络舆情热度和水污染程度都存在正相关关系。网络舆情热度越高,水污染程度越大,政府关注度自然会越高。
18 政府参与度 100-32.34×EXP(-0.000723×新闻
发布量)
模型假设政府新闻发布量能够代表政府参与度,其中权重值参考文献[21]。
19 官方新闻透明度 0.73 取自300位网民的问卷数据。打分取值范围在(0,1),用算术平方法计算最终分数。
20 事件处理满意程度 73.77 取自300位网民的问卷数据。打分取值范围在(0,100),用算术平方法计算最终分数。
21 政府响应时间 1 取自人民网所发布的新闻数据。事件发生至政府发声时间将近一天。
22 政府公信力 (事件处理满意程度-政府响应时间×5)×官方新闻透明度/2.5 政府公信力由这三个因素直接影响,经过反复调整得到的公式。
23 政府危机处理力度 71.25 取自300位网民的问卷数据。打分取值范围在(0,100),用算术平方法计算最终分数。
24 网民对政府满意度 0.360×政府公信力+0.851×政府危机处理力度+0.091×政府参与度 网民对政府满意度受到这三个因素的直接作用,其中权重值由专家打分法得到,最终权重结果采用各个专家平均值。
水体污染情况 25 Time 1 是时间变量,设置初值为第一天,仿真过程中可自由控制。
26 水体污染超标倍数 64.20 取自人民网所发布的新闻数据。
27 水体污染程度 水体污染超标倍数/3×EXP(-“<Time>”) 水体污染程度与事件刚发生时水体污染超标倍数有关,而且会随着时间的流逝慢慢降低。
Variable Description
事件 水体主要
污染物
水体污染
超标倍数
政府响应
时间
2019年3月21日 苯胺类 64.2倍 1天
Relevant Data of the Xiangshui Explosion Accident
变量名称 问题个数 最终得分 评分范围
网民自身情况 3个 / /
官方信息透明度 2个 0.73 (0,1)
危机处理力度 3个 71.25 (0,100)
事件处理满意程度 2个 75.77 (0,100)
Variables and Questions in Questionnaire
变量

天数
1 2 3 4 5 6 7 8 9 10 11 12
网民微博发帖量 3 870 1712 726 766 841 520 719 227 142 68 78 123
网媒新闻发布量 85 101 38 23 25 8 13 2 2 3 2 1
Online Public Opinion Statistics in Xiangshui Explosion Accident
Netizen Weibo Posts
Online News Releases
Netizen Attention
Internet Public Sentiment
Netizen Weibo Post Volume Test
Network News Release Volume Test
变量 MAPE值 结论
网民微博发帖量 18% 良好预测
网媒新闻发布量 27% 可行预测
MAPE Value of Simulation Results
Impact of Changes in Processing Intensity on Public Opinion
Impact of Changes in Official News Transparency on Public Opinion
Impact of Changing Government Response Time on Spread of Public Opinion
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