## 3 Materials and Methods
### Methods
#### 3.3.1 Analysis of User Sentiments and Concerns Information
The UCI equation is as follows:  
\[UCI=\alpha PR(v_{i})+\beta C_{dn}(v_{i})+\gamma C_{e}(v_{i}) \tag{7}\]  
where \(v_{i}\) represents the current node; \(PR(v_{i})\) is the PageRank value of the \(v_{i}\) node, reflecting the importance of the keyword in the net; \(C_{dn}(v_{i})\) is the result of  
where \(C_{e}(v_{i})\) is the eigenvector centrality of the current node \(v_{i}\), and \(v_{j}\) denotes that of the other nodes; \(\lambda\) is the maximum eigenvalue of the eigenvector; and \(A_{ij}\) is the adjacency matrix of the graph. \(C_{dn}(v_{i})\) is calculated as follows:  
\[C_{dn}(v_{i})=\frac{C_{d}(v_{i})}{max(C_{d}(v_{j}))} \tag{10}\]  
\[a_{i}=g_{1}(W_{a}\cdot C_{i}+b_{a}) \tag{1}\]0  
where \(d(v_{i})\) denotes the actual number of connections at node \(v_{i}\), \(N\) represents the number of nodes in the network, and \(1\leq j\leq N\), \(max(C_{d}(v_{j}))\) represents the maximum value of the degree centrality of the points in the network. 3.2 Representation of Knowledge Elements Pertaining to Typhoon Cascading Disasters Incorporating User Concern Information  
A knowledge element represents an indivisible unit of knowledge that embodies a complete expression of knowledge. As the foundational unit of knowledge management, it is composed of three components: objects, attributes, and relationships. A knowledge element model is typically characterized by three key components: the object knowledge element \(K_{m}\), the attribute knowledge element \(K_{a}\), and the relationship knowledge element \(K_{r}\). In the process of disaster scenario modeling, knowledge element models serve as a tool to comprehensively describe the entire life cycle of disaster events. For instance, when modeling a disaster scenario, these knowledge elements can effectively depict various aspects of the event, including the nature of the scenario, emergency rescue activities, etc. When integrated with DBNs, knowledge element models can accurately simulate the dynamic evolution of disasters. This study investigates the relationship between disaster scenario evolution, emergency management measures, and public opinion. By focusing on this relationship, the study extracts the critical scenario elements of typhoons and their cascading effects, and employs knowledge theory to analyze these scenario elements. Currently, there are different standards for dividing scenario elements. [PERSON] et al. divided disaster scenario elements into four aspects: disaster events, disaster-causing factors, disaster-bearing entities, and emergency activities [48]. [PERSON] et al. classified disaster scenario elements as driving factors, emergency measures, and emergency targets [49]. Considering the characteristics of the stages of online public opinion, this study integrates data on the cascading effects of typhoons, the factors dynamically influencing the network, and the roles of various users. Through systematic analysis, it identifies the key scenario elements of typhoons and their cascading effects, corresponding to the main nodes in the DBNs. These elements include disaster events, the external environment, driving factors, emergency activities, and emergency targets, represented by \(S=\{C,E,D,M,T\}\), as shown in \[a_{i}=g_{1}(W_{a}\cdot C_{i}+b_{a}) \tag{1}\]1  
Driving factors \((D)\) mainly refer to the factors affecting the evolution process of typhoons and their cascading effects, and contain both objective and subjective factors. In this study, subjective driving factors specifically refer to user concern information extracted from the online public discourse regarding typhoons and their cascading effects. In this study, there are two primary types of outcomes produced by the driving factors: a positive outcome that pushes the typhoon event in a positive direction, and a negative outcome that pushes the typhoon event in a negative directioClarifying the relationships among various scenario elements is essential for constructing scenario deduction models.