突发自然灾害事件网络舆情传播特征及影响因素研究

随着网络技术的发展,网络舆情分析在应对突发事件中发挥的作用日益显著。自然灾害发生后,准确把握舆情信息传播特征并分析其影响因素有助于应急管理部门及时采取有效的应急救援措施。本文以台风“利奇马”为例,基于“新浪舆情通”系统搜集的相关微博、微信、论坛、网站等全网舆情数据,探究台风灾害全过程舆情信息的时空分布特征,开展灾害舆情信息影响因素相关性分析。研究表明：① 相比于灰色EGM(1, 1)模型,ARIMA模型对于舆情的短时预测具有较高的适用度,所预测的舆情信息的时序变化与利奇马台风的生命周期相符;② 舆情的空间分布具有聚集性,其分布与受灾程度呈正相关关系,但同时受灾区经济状况和网络普及率影响;③ 灾情严重程度与原创舆情信息的相关性高于转发舆情信息,原创舆情信息更能反映受灾地区的实际受灾情况。研究内容为应急管理部门及时掌握舆情走势并调整应急救助决策提供了指导价值。

Research On the Dissemination Characteristics and Influencing Factors of Network Public Opinion of Sudden Natural Disaster Events

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.