## 4 Experiments
### Propagation Graph Analysis  
In order to explore the impact of propagation paths on disinformation detection, we statistically analyze the structure of the propagation networks of disinformation and non-disinformation, aiming to reveal the differences in propagation patterns between the two. We integrated the labels of the Twitter15 and Twitter16 datasets, classified \"verified true rumors (TR)\", \"verified false rumors (FR)\", and \"unverified rumors (UR)\" into the category of \"rumors\", and compared them with the data of \"Non-rumors (NR)\". The visualization of information dissemination is shown in Figure 5 below. In an information dissemination relationship graph, each node represents a separate unit of information, which includes the original tweet, its related comments, or retweets. These nodes are connected by edges, which represent interactive behaviors between them, such as retweets or comments. We define the original tweet as the root node of the relationship graph, and all posts that directly reply to that tweet become its children. Following this logic, if a post \(-i\) receives a reply from another post \(-j\), then according to the order of information dissemination, post \(-j\) becomes a child node of post \(-i\), which is represented in the graph as node \(-j\) is a subordinate node of node \(-i\). As shown in Figure 5, the information dissemination tree exhibits a broad structure in which most nodes belong to the shallow first-level responses [52]. In the disinformation dissemination network, there are obvious clusters of nodes, and the nodes within the cluster are more densely connected, while the nodes connected to nodes outside the cluster are relatively sparse, which reflects the high degree of aggregation in the disinformation dissemination network. This discrepancy may be explained by the fact that well-crafted rumors usually carry \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**ACC**} & \multicolumn{4}{c}{**F1**} \\ \cline{3-6}  & & **NR** & **FR** & **TR** & **UR** \\ \hline w/o Bi-Ps & 0.869 & 0.821 & 0.945 & 0.857 & 0.833 \\ w/o Ps & 0.838 & 0.830 & 0.941 & 0.777 & 0.751 \\ w/o Text & 0.842 & 0.735 & 0.653 & 0.956 & 0.764 \\ ICP-BGCN & 0.897 & 0.904 & 0.959 & 0.850 & 0.869 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of ablation experimental results on Twitter15 dataset. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**ACC**} & \multicolumn{4}{c}{**F1**} \\ \cline{3-6}  & & **NR** & **FR** & **TR** & **UR** \\ \hline w/o Bi-Ps & 0.889 & 0.769 & 0.671 & 0.957 & 0.903 \\ w/o Ps & 0.855 & 0.802 & 0.867 & 0.944 & 0.477 \\ w/o Text & 0.843 & 0.791 & 0.665 & 0.933 & 0.805 \\ ICP-BGCN & 0.917 & 0.931 & 0.891 & 0.962 & 0.883 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of ablation experimental results on Twitter16 dataset. inherent information content features that can trigger the replies and reposts of multiple internet celebrity individuals with high social influence. In contrast, naturally occurring true events are not well crafted to maximize their social impact, making it non-trivial to trigger reposts or replies of multiple internet celebrity individuals simultaneously. In addition, the structure of the disinformation dissemination network is more complex, the dissemination path is usually longer, and the connections between nodes are closer, which suggests that the rumor information not only spreads quickly in the process of dissemination but also gets strengthened and consolidated in specific groups. In contrast, a lower aggregation trend is presented in the dissemination network of non-false information. Its path distribution is more uniform and the connection between nodes is sparse, all these features indicating that the breadth of information dissemination is limited and decentralized.