## 4 Experiments
### Results and Analysis
Experimental results demonstrate that ICP-BGCN achieves an accuracy of 84.4%, representing a 1% improvement over the best baseline model (GCAN: 83.4%), while also delivering competitive performance in precision, recall, and F1-score. This finding illustrates that the model not only effectively identifies routine rumor patterns in Twitter15 and Twitter16 datasets but also exhibits robust performance in handling Pheme's high-emotion, semantically ambiguous crisis-related misinformation. This cross-domain adaptability highlights its robustness and potential for generalization across diverse propagation scenarios. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Class** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{2}{*}{GCAN [33]} & R & \multirow{2}{*}{0.834} & 0.769 & 0.758 & 0.761 \\  & N & & 0.871 & 0.874 & 0.872 \\ \hline \multirow{2}{*}{Bi-GCN [11]} & R & \multirow{2}{*}{0.824} & 0.753 & 0.734 & 0.741 \\  & N & & 0.861 & 0.872 & 0.865 \\ \hline \multirow{2}{*}{GACL-CADA [48]} & R & \multirow{2}{*}{0.808} & 0.523 & 0.722 & 0.599 \\  & N & & 0.921 & 0.831 & 0.872 \\ \hline \multirow{2}{*}{GAN [49]} & R & \multirow{2}{*}{0.823} & 0.765 & 0.760 & 0.760 \\  & N & & 0.858 & 0.858 & 0.857 \\ \hline \multirow{2}{*}{ICP-BGCN} & R & \multirow{2}{*}{0.844} & 0.770 & 0.794 & 0.775 \\  & N & & 0.889 & 0.870 & 0.775 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results on Pheme dataset. To further ensure the accuracy of our results and assess the generalization capability of our proposed ICP-BGCN model, we incorporate the SemEval-17-task 8 dataset as an additional benchmark in our experiments. The SemEval-17-task 8 dataset is widely adopted for rumor analysis and provides a rich set of Twitter conversational threads with fine-grained labels--\"True rumor\" (TR), \"False rumor\" (FR), and \"Unverified rumor\" (UR)--as well as stance classification tasks. The experimental results are shown in Table 6. The technical specifications of the four baseline models are as follows. HiTPLAN [10] employs a multi-level Transformer architecture to capture nuanced contextual representations from social media posts for deceptive content detection. MTL2-Hierarchical Transformer hierarchically [50] segments conversational threads into sub-threads, encodes contextual features using BERT embeddings, and aggregates cross-sub-thread semantics via Transformer fusion to enable multi-granular representation learning. Coupled Hierarchical Transformer extends MTL2 [50] by integrating multi-task learning through a hybrid attention mechanism that aligns BERT-derived semantics with stance-aware propagation patterns, jointly optimizing rumor verification and stance detection. Hierarchical Contrastive Disentangled Multi-task Graph Network (HCD-MGN) [51] enhances multi-task performance through (1) a feature decoupling module (PFN) separating shared/task-specific features, (2) dual graph encoders modeling propagation structures and semantic relationships, and (3) stance-aware contrastive learning for representation optimization. On the SemEval-17 Task 8 dataset, the proposed ICP-BGCN model achieves state-of-the-art performance with 78.5% accuracy and 79.2% Macro-F1 score, demonstrating a 1.8% absolute performance improvement over the best baseline model HCD-MGN (76.7% accuracy). These results, together with those obtained from the Twitter15, Twitter16, and Pheme datasets, confirm that our model consistently generalizes across multiple social data sources, effectively capturing the unique propagation structures inherent in different social media scenarios.