## 4 Experiments
### Propagation Graph Analysis
Further, we computed and compared the topological metrics of the information dissemination graphs, such as the number of nodes, number of edges, average path length, degree distribution, etc. We used the average values of the metrics for all graphs in both categories, non-spurious information and spurious information, as the final comparison  
Figure 5: Comparison of information dissemination network diagrams. (**a**,**c**) represent the dissemination networks of typical non-disinformation events in the two datasets, respectively; and (**b**,**d**) represent the dissemination maps of typical disinformation in the two datasets, respectively. data. The calculation results of the network structure metrics are detailed in Table 9; see Figure 6 for the visualization of the degree distribution. In the network metrics calculation results shown in Table 9, the results of the two datasets show opposite trends. Specifically, in the Twitter15 dataset, network metrics such as the number of nodes and the number of edges present higher values in the non-rumor category; however, in the Twitter16 dataset, the values of these same network metrics in the rumor category exceed those in the non-rumor category. According to the relevant literature discussion, false information spreads farther, faster, deeper, and wider in terms of speed, scope, depth, and breadth compared to non-false information [3; 54; 53]. In addition, the experimental results in Section 4.3 show that disinformation detection based on propagation paths performs significantly better in the Twitter16 dataset than in the Twitter15 dataset. Therefore, we believe that the network characteristics revealed by the Twitter16 dataset are more in line with the characteristics displayed by disinformation and non-disinformation during the propagation process, where key indicators, such as the number of nodes, the number of edges, the graph diameter, and the average path length of disinformation, are larger than those of non-disinformation. That is, there is more user participation, more frequent forwarding of information, a wider dissemination range, and more complex dissemination paths in the dissemination process of disinformation. Due to its unrepresentative sample failing to encompass a broader or more balanced range of user groups and message types, the Twitter15 dataset may not exhibit network characteristics consistent with theory. In summary, the Twitter16 dataset is more consistent with the characteristics of real rumor propagation, and our method is more valid for this type of data. Figure 6: Plot of the degree distribution of the dataset. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Indictors**} & \multicolumn{2}{c}{**Twitter15**} & \multicolumn{2}{c}{**Twitter16**} \\ \cline{2-5}  & **Non-Rumor** & **Rumor** & **Non-Rumor** & **Rumor** \\ \hline Nodes & 52.786 & 51.369 & 43.458 & 50.829 \\ Edges & 103.572 & 100.738 & 84.915 & 99.657 \\ Diameter & 6.875 & 6.838 & 6.592 & 7.081 \\ Average Path Length & 2.676 & 2.715 & 2.612 & 2.791 \\ Assortativity & \(-0.740\) & \(-0.763\) & \(-0.743\) & \(-0.748\) \\ Density & 0.114 & 0.113 & 0.154 & 0.127 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Calculated network metrics for datasets. In addition, by analyzing the coefficient of congruence and network density values, the analysis reveals that the information dissemination network is a low-density heterogeneous network. In such a network structure, the connections between nodes are not tightly connected, but show a tendency to connect between highly connected nodes and lowly connected nodes. This phenomenon suggests that a small number of nodes in the information dissemination network, i.e., those highly connected nodes, which play a key role in the network, have a significant impact on the overall performance of the network.