## 5 Discussion
### Analysis of Scenario Deduction Results for Typhoon Behinca and Its Cascading Disasters  
The Bayesian network scenario model shown in Figure 9 provides an abstract representation of the conceptual attributes, internal and external connections, and development process of typhoons and their cascading disaster events, driven by online public opinion. It reveals the inherent laws and change logic of various disaster scenarios, which could help decision makers to quickly intervene in developing scenarios and shift sentiments towards optimism. In addition, according to the modeling results, the probabilities of the occurrence of typhoons and their cascading disaster events \(C_{1}\), \(C_{2}\), \(C_{3}\), \(C_{4}\), \(C_{5}\), \(C_{6}\), \(C_{7}\), \(C_{8}\) (a sensitivity relationship diagram of eight main node variables is shown in Figure S3, from the generation and development of online public opinion to stable public opinion, are 58%, 68%, 72%, 76%, 82%, 79%, 73%, and 64%, respectively, as shown by the red boxes in Figure 10. This shows that under reasonable emergency intervention, public perceptions of the typhoon and its cascading disaster events continue to evolve in a positive direction, and the deduction process of the scenario model is basically consistent with the impact of the actual typhoon (which caused a storm surge, rainstorm waterlogging, etc.) on the city; this verifies the rationality and reliability of the model. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Positive Sentiments**} & \multicolumn{4}{c}{**Negative Sentiments**} \\ \cline{2-10}
**Data** & **Page** & **Degree** & **Eigenvector** & **UCI** & **Page** & **Degree** & **Eigenvector** & \\  & **Rank** & **Centrality** & **Centrality** & **UCI** & **Rank** & **Centrality** & **Centrality** & **UCI** \\ \hline Drainage & 0.0011 & 83 & 0.0529 & 0.0165 & 0.0002 & 35 & 0.0441 & 0.0137 \\ Waterlogging & 0.0010 & 93 & 0.0849 & 0.0235 & 0.0002 & 25 & 0.0395 & 0.0114 \\ Slucie & 0.0010 & 74 & 0.0437 & 0.0140 & \(\backslash\) & \(\backslash\) & \(\backslash\) & \(\backslash\) \\ Rainstorm & 0.0104 & 857 & 0.3825 & 0.1370 & 0.0056 & 418 & 0.2941 & 0.1186 \\ Seepage & 0.0007 & 43 & 0.0202 & 0.0072 & 0.0011 & 34 & 0.0240 & 0.0100 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Information users focus on during rainstorm disasters under positive and negative sentiments. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Positive Sentiments**} & \multicolumn{4}{c}{**Negative Sentiments**} \\ \cline{2-10}
**Data** & **Page** & **Degree** & **Eigenvector** & **Page** & **Degree** & **Eigenvector** & \\  & **Rank** & **Centrality** & **Centrality** & **UCI** & **Rank** & **Centrality** & **Centrality** & **UCI** \\ \hline Strength & 0.0049 & 1184 & 0.4641 & 0.1716 & 0.0034 & 343 & 0.2310 & 0.0947 \\ Expect & 0.0122 & 1283 & 0.5068 & 0.1902 & 0.0042 & 368 & 0.2807 & 0.1084 \\ Rescue & 0.0020 & 267 & 0.1343 & 0.0451 & 0.0005 & 58 & 0.0549 & 0.0191 \\ Guard & 0.0032 & 497 & 0.2244 & 0.0786 & 0.0005 & 40 & 0.0450 & 0.0147 \\ Frightening & 0.0001 & 43 & 0.0231 & 0.0074 & 0.0019 & 43 & 0.0453 & 0.0159 \\ Anxiety & 0.0000 & 29 & 0.0150 & 0.0049 & 0.0011 & 36 & 0.0386 & 0.0132 \\ Worry & 0.0002 & 153 & 0.0773 & 0.0255 & 0.0007 & 94 & 0.0784 & 0.0289 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Information users focus on under positive and negative sentiments (partial).