## 4 Experiments
### Results and Analysis
It is also better than other models in various indicators, which shows that it is reasonable and effective to fully fuse the original text features, propagation text features, and propagation structure features to improve the accuracy of false information detection. Overall, the ICP-BGCN model outperforms the other eight models, which include traditional machine learning and deep learning approaches, in terms of detection accuracy and F1 score for each category to varying extents. To comprehensively assess the cross-scenario generalization capability of our proposed ICP-BGCN model, we select the Pheme dataset with significant scenario variations as \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 DTC & 0.465 & 0.643 & 0.393 & 0.419 & 0.403 \\ SVM-RBF & 0.553 & 0.553 & 0.553 & 0.553 & 0.553 \\ GRU & 0.633 & 0.772 & 0.489 & 0.686 & 0.593 \\ RVNN & 0.737 & 0.737 & 0.737 & 0.737 & 0.737 \\ PPC\_RNN + CNN & 0.690 & 0.690 & 0.690 & 0.690 & 0.690 \\ Bi-GCN & 0.880 & 0.847 & 0.869 & 0.937 & 0.865 \\ GCN-Bert & 0.877 & 0.915 & 0.827 & 0.886 & **0.930** \\ HAGNN & 0.874 & 0.815 & 0.809 & 0.880 & 0.865 \\ ICP-BGCN & **0.917** & **0.931** & **0.891** & **0.962** & 0.883 \\ \hline \hline \end{tabular} Bold indicates optimal results.  
\end{table} 
Table 4: Comparative experimental results on Twitter16 dataset. our validation benchmark. As a misinformation detection benchmark dataset in breaking news scenarios, Pheme encompasses multiple crisis domains, including social, political, and health-related events [46], with its cross-domain characteristics providing an ideal experimental environment for evaluating model adaptability across different scenarios. Compared to conventional datasets, like Twitter15 and Twitter16, Pheme exhibits three distinctive characteristics. Firstly, in terms of data composition, the dataset contains high-risk events with heightened emotional intensity (e.g., public health crises, political scandals) and user interactions exhibiting more pronounced emotional signals [47], presenting multidimensional challenges for semantic modeling. Secondly, the Pheme dataset exhibits significant variations in event scales, where much of the information labeled as rumors actually originates from misclassifications of real events [46]. This label distribution characteristic constitutes a rigorous test of the model's discriminative power. Thirdly, regarding data scale, the limited rumor samples (with non-rumors constituting 63.5% of instances) intensify training challenges under class imbalance conditions [46]. These characteristics closely align with the complex scenarios of misinformation propagation in real-world settings. To validate ICP-BGCN's performance on cross-scenario datasets, we maintain identical parameter configurations to those used in Twitter15 and Twitter16 experiments, conducting comparative analyses with four baseline models. The experimental results for the Pheme dataset are shown in Table 5.  
The four baseline models employ distinct technical approaches. GCAN [33] utilizes graph neural networks with dual co-attention mechanisms to achieve multimodal dynamic feature fusion across source text, user attributes, and propagation pathways. Bi-GCN [11] leverages bidirectional graph convolutional networks to concurrently model forward diffusion (source-to-retweeters) and reverse traceability (leaf-to-source) patterns in information dissemination. GACL-CADA [48] implements a class-aware adversarial domain adaptation framework to address cross-domain distribution alignment between historical data and emerging events. GAN [49] enhances detection robustness for hybrid true/false content through adversarial sample generation (e.g., semantically ambiguous text variants) coupled with discriminator-based decision boundary optimization.