摘要
突发性公共危机事件因其具有爆发性、特殊性,环境复杂性、演变不确定性、群体扩散性等特征,为应急管理带来非常大的风险。本研究从网络舆情角度出发,研究突发性公共危机事件与网络舆情的作用机制,为突发性公共危机事件的管理、应对工作提供理论支撑。本研究的主要成果如下：
(1)提出了以内源动力与外源动力为主体的网络舆情态势演化动力机制。
通过对突发性公共危机事件与网络舆情作用机制以及态势演化路径的分析与解读,从微观层面上提出了以内源动力与外源动力为主体的网络舆情态势演化动力机制。将网络舆情态势演化的作用力分为来自于事件本身的破坏力、来自于网络的推动力以及来自于政府的调控力；并根据这三种力对舆情态势的作用特点,结合其力的本质特征,将其划分为内源动力与外源动力；在此基础之上,通过与28位专家进行交流和访谈,解构出34个内源动力与外源动力的构成因素,经过统计、提炼和总结,并结合已有知识,最终确定内源动力与外源动力的构成因素分为四级共22个因素；其中内源动力的构成因素包括事件敏感度与事件公共性,其中,在事件敏感度的敏感因素抽取中,通过对2008年至2012年60个网络热点突发性公共危机事件进行汇总、分析和总结,共抽取出12个敏感因素；外源动力构成因素包括3个一级指标、6个二级指标、4个三级指标和5个四级指标。该成果为成果2、成果3、成果4提供了理论支撑。
(2)基于系统动力学的突发性公共危机事件与网络舆情作用机制的动力学建模。
以系统动力学为工具,通过梳理突发性公共危机事件与网络舆情之间的复杂关系,从宏观层面上构建了反映突发性公共危机事件网络舆情作用机制的动力学模型。模型由政府、网络媒体、网民三个子系统组成,包含35个变量、39个反馈环以及一类典型基模；通过采集2008年至2011年36件突发性公共危机事件的相关数据,结合85份调查问卷(问卷各变量信度系数a>0.7,效度检验显著)用于变量之间定量关系的获得；选取活跃总贴数、网络新闻数量等两个指标,将真实值与预测值进行拟合,发现MAPE误差比属于10% (3)基于贝叶斯网络建立突发性公共危机事件网络舆情热度预警模型。
通过对已有研究的综述与分析,结合突发性公共危机事件特点(爆发性、特殊性,环境复杂性、演变不确定性、群体扩散性等)以及贝叶斯网络的计算特征(复杂关联关系表示能力、概率不确定表示能力以及因果推理能力),从中观层面建立了基于贝叶斯网络建立的舆情热度预警模型。主要包括三个步骤,第一,贝叶斯网络结构确立。将致使网络舆情热度形成原因分为两个层次,处于底层的为内源动力(事件破坏力),处于直接影响层的为外源动力中的网络推动力(包括网民推动力与网媒推动力)；在此基础上,将贝叶斯网络结构分为三个层次,分别是结果层(网络舆情热度预警结果)、结构层(网民推动力、网媒推动力和事件破坏力)以及数据层(包括网络媒体推动力相关数据、反映事件破坏力的相关数据、反映网民推动力的相关数据),并将贝叶斯网络中的每个数据确定为三种状态,即高、中、低；第二,条件概率确定。条件概率学习的数据来源于83件突发性公共危机事件；结合专家经验以及变量自身特点获得连续数据的离散化标准；通过多种算法的比照,最终确定使用期望最大算法(EM算法)进行条件概率学习。第三,借助Netica仿真工具进行仿真。首先通过测试集数据的真实值与预测值进行比较,验证了模型的可靠性；在模型应用演示中,假设模型获得了新证据(将事件敏感度、事件关联性与不可选择性作为新证据),舆情热度处于高、中、低的可能性变为24%、30.8%和45.2%,从而得出预警结论：网络舆情热度所处状态的可能性为高。综上本研究为网络舆情热度预警提供了一种新思路,这种方式能够改善目前在预警中存在的主观、滞后等问题,使舆情热度预警过程更加自动、智能、高效,成果3对突发事件应对工作的开展具有积极意义。
(4)基于复杂系统理论建立突发性公共危机事件内外源动力耦合模型。
从微观层面出发,对内源动力与外源动力这两大模块之间耦合机制进行探索,基于复杂系统理论建立突发性公共危机事件网络舆情耦合模型。主要分为三个步骤,第一,建立指标体系。通过因素梳理,分别建立内源动力与外源动力的测量指标体系,并通过BP神经网络方法求得各指标的权重；第二,建立耦合度模型。以指标体系为基础,以16个突发性公共危机事件为研究对象,借助物理学中的容量耦合模型、耦合协调度模型等将内源动力模块与外源动力模块之间的耦合度定量化,最终发现这16个事件的内源动力与外源动力之间都存在或强或弱的耦合关系(其中存在高度耦合关系的有6个,极度耦合关系的有1个)；第三,实证研究。选取“新闻数量”及“新闻回复数”这两个变量作为反映突发性公共危机事件社会影响力的两个定量指标,利用SPSS软件与这16个事件的内源动力与外源动力的耦合协调度进行相关性分析,发现它们之间具有明显的正相关关系(其中,耦合协调度与新闻数量的相关系数为0.692、耦合协调度与新闻回复数之间的相关系数为0.633),由此证明内源动力与外源动力之间的耦合度越高,事件的社会影响力越大。基于此,本研究给出四条应用建议切断耦合,从而将事件的负面影响降至最低。这些建议包括：建立敏感词库、有效预警、合理有力的调控、重视辅助的技术手段等。
The unexpected public crisis takes risk and difficulties to the crisis management, because of its characteristics such as explosion, particularity, environmental complexity, evolving uncertainty and group diffusion. From the perspective of online public opinion, this research focuses on the mechanism between unexpected public crisis and online public opinion. It provides theoretic supports for the crisis management. The main results of this study are as follows:
(1) Putting forward a dynamic mechanism of online public opinion evolution in which internal driving power and exogenous driving power are the main part.
Through the analysis of mechanism and the evolution path of unexpected public crisis and online public opinion, from the theoretical perspective, we put forward a dynamic mechanism of online public opinion evolution which mainly includes internal driving power and exogenous driving power. The power which impel the situation of online public opinion begin to evolution including the destructive power from the crisis itself, the impetus from the network by netizen and network media, and the regulation power from the government agencies; According to their essential characteristics, we divide these three elements into two parts, the internal driving power and exogenous driving power. At the beginning, we deconstruct34elements for internal driving power and exogenous driving power by communicating with28experts. Through statistic analysis and summarizing, we choose22elements from34to be the most important ones.The main elements of internal driving power include the sensitivity and publicity of the crisis.In the experimental research for sensitivity factors, we studied60unexpected and influencial public crises from2008to2012, and concluded that there were12most sensitive factors; The mainly constituent elements of exogenous driving power rank into three first-class indicators, six second-class indicators, four third-class indicators and five forth-class indicators. This result provides the theoretical supports for the other conclusions in this research.
(2) We established a dynamic model based on the mechanism between unexpected public crisis and online public opinion.
After studying the complicated relationship between unexpected public crisis and online public opinion, we built a dynamic model by system dynamics method describing the mechanism between unexpected public crisis and online public opinion from the macro perspective. This system dynamics model is constructed by three subsystems, including the government agencies, network media and netizen. There are35variables,39feedback loops and one kind of typical systems archetype in the SD model. The data come from36unexpected public crises and85questionnaires (reliability coefficient a>0.7, P<0.05). In order to validate the reliability of the SD model, we compare the true value and the predictive value of two variables, which are the number of BBS boots and network news. The MAPE value is between10%and20%, proving the reliability of SD model in the previous literature, there was no similar research. This study is definitely positive and innovative. We analyze four moderator variables from12variables, and get16conclusions. These four variables are the sensitivity of the crisis, the publicity of the crisis, the credibility of the government, and the query degree of for the crisis. This research concludes seven suggestions for dealing with unexpected public crisis and directing the online public opinion.
(3) We built an early-warning model to predict the hot degree of online public opinion on the basis of Bayesian network.
After considering the features of Bayesian network, such as the expressing ability for variables'complex relationship, the calculating ability for the uncertainty probability and the reasoning ability for cause-effect relationship, we built the early-warning model to pridict the hot degree of online public opinion on the basis of Bayesian network from the medium perspective. There are three steps. Firstly, we construct the structure of the Bayesian network. As we all know, the internal driving power and exogenous driving power push crisis into a hot online topic. As a result, we divide Bayesian network into three layers. They are results layer (the results of early-warning), the structural layer (the main structure of Bayesian network) and the data layer (the simulation data for the medal). And every data in Bayesian network is ranked into low, middle or high level. Secondly, we determine the conditional probability of the Bayesian network. The data for conditional probability study come from83unexpected public crises. Converging experts'experience andvariables'features, we confirm the standard to discretize the continuous data. After comparing many kinds of algorithms, finally we use EM algorithms for conditional probabilitystudy. Thirdly, using Netica for simulation. We use the testing set to verify the effect of the model and we ultimately prove the reliability of our model. In the simulation, we supposed that we gain the new evidences (the sensitivity of the crisis, the reliability of the crisis and the unselectable of the crisis), and the simulation result show that the probable situation of the online public opinion is low (24%), middle (30.8%) and high (45.2%). So the conclusion is that the situation of the online public opinion will be hot. As stated previously, we put forward a new method for online public opinion early-warning study. This method will make the early-warning process more intelligent, automatic and efficient.
(4) We built a coupling model for unexpected public crisis and online public opinion based on complex systems theory.
From the micro perspective, we built the coupling model for unexpected public crisis and online public opinion based on complex systems theory. There are three steps. Firstly, we contribute an index system to quantize the degree of the internal driving power and exogenous driving power. And we use BP neural network algorithm for calculating the weight of the indexes. Secondly, we establish the coupling model. Based on the index system, we established the coupling model to quantize the coupling degree between internal driving module and exogenous driving module. We select16unexpected public crises to calculate their coupling degrees. Finally, we found that there is strong or weak coupling relationship among16crisis's modules:6crises have the highly coupled relationship, and one crisis has the extreme coupling relationship. Thirdly, we make the empirical research. In the empirical research, we choose two quantitative indicators including the number of network news and the number of network news reply, which can reflect the social influence of the unexpected public crisis. And then, we use SPSS to analyze the correlativity between the coupling degree of the modules from the16crises and the two quantitative indicators. Ultimately, we found there is a significantly positive correlation among them. This result proves that the greater the coupling degree is, the higher the social influence of the unexpected public crisis is. Based on these, in order to reduce the negative social influence to the lowest level, we give four applicative suggestions to cut off the coupling, They are establishing the sensitive factors thesaurus, building the early warning system, regulating effectively by government and the setting up high-efficiency online public opinion monitoring platform.
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