Journal of Geo-information Science >
Research On the Dissemination Characteristics and Influencing Factors of Network Public Opinion of Sudden Natural Disaster Events
Received date: 2020-09-12
Request revised date: 2020-10-26
Online published: 2021-08-25
Supported by
National Key Research and Development Program of China(2018YFC1508900)
National Key Research and Development Program of China(2017YFC1503000)
Copyright
With the development of network technology, the analysis of internet public opinion plays an increasingly important role in dealing with the emergency. After the occurrence of natural disasters, it is helpful for the emergency management department to take effective emergency rescue measures in time to accurately grasp the characteristics of public opinion information and analyze its influencing factors. Based on the network public opinion data related to Typhoon Lekima, including micro-blog, WeChat, forums, websites, and other online public opinion data collected by the "Public opinion on Sina" system, this article analyzes the spatiotemporal characteristics of disaster public sentiment in the process of disaster. The influencing factors of the disaster public opinion information are also analyzed. The results show that the temporal distribution of public opinion information is consistent with the lifecycle of Typhoon Lekima. Compared with the grey EGM (1,1) model, ARIMA model has a higher applicability for short-term prediction of public opinion. The spatial distribution of public opinion is positively related to the severity of the disaster and also related to the economic condition and the network popularity in the affected area. The correlation between the severity of the disaster and the original public opinion information is stronger than that between the severity of the disaster and the transmitted public opinion information. The original public opinion information can better reflect the actual situation of affected areas. The study provides guidance for emergency departments to grasp the trend of public opinion and adjust emergency measures timely.
Key words: Lekima; Typhoon; disasters; internet public opinion; time and space analysis; microblog; emergency; rescue
ZHAO Fei , LIAO Yongfeng . Research On the Dissemination Characteristics and Influencing Factors of Network Public Opinion of Sudden Natural Disaster Events[J]. Journal of Geo-information Science, 2021 , 23(6) : 992 -1001 . DOI: 10.12082/dqxxkx.2021.200526
表1 全局莫兰指数分析结果Tab. 1 Analysis results of global Moran's I |
空间权重矩阵构建方法 | 莫兰指数 | Z值 | p值 |
---|---|---|---|
反距离法 | 0.0459 | 0.9513 | 0.3414 |
距离范围法 | 0.0637 | 1.3192 | 0.1870 |
共边邻接法 | -0.1374 | -1.6360 | 0.1018 |
k近邻法(k=4) | 0.1281 | 1.6327 | 0.1025 |
k近邻法(k=5) | 0.0922 | 1.4159 | 0.1567 |
k近邻法(k=6) | 0.0743 | 1.3421 | 0.1795 |
k近邻法(k=7) | 0.0436 | 1.0509 | 0.2932 |
k近邻法(k=8) | 0.0583 | 1.3743 | 0.1693 |
k近邻法(k=9) | 0.0694 | 1.6975 | 0.0895 |
k近邻法(k=10) | 0.0422 | 1.3554 | 0.1752 |
表2 局部莫兰指数分析结果Tab. 2 Analysis Results of Local Moran's I |
省份 | 舆情数量/起 | 局部莫兰指数 | Z值 | p值 | 聚集类型 |
---|---|---|---|---|---|
山东 | 730 950 | 0.9901 | 3.7047 | 0.000212 | H-H |
浙江 | 667 165 | 0.6154 | 2.3446 | 0.019047 | H-H |
广东 | 761 821 | -1.2235 | -4.3323 | 0.000015 | H-L |
表3 逐步回归分析参数Tab. 3 Stepwise regression analysis parameters |
变量 | 模型1 | 模型2 | 模型3 | 模型4 |
---|---|---|---|---|
-0.493 | -1.474 | -0.708 | -0.476 | |
1.187 | 1.038 | 0.969 | 0.959 | |
4.487 | 4.129 | 3.386 | ||
0.035 | 0.037 | |||
0.085* | ||||
0.805 | 0.868 | 0.887 | 0.889 |
注:*表示未通过95%显著性水平检验。当加入的变量多于5个时,各项变量的影响系数的显著性水平逐渐变差,因此未列出。 |
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