## 4 Experiments
### Results and Analysis  
On Twitter15 and Twitter16 datasets, the proposed ICP-BGCN method is analyzed with seven baseline models such as the classical DTC, and the experimental results are shown in Tables 3 and 4. \begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Experimental Setup** \\ \hline OS & Ubuntu22.04 \\ Development environment & VScode 1.98.2 \\ processor & Intel(R)Xeon® Silver 4116 CPU @ 2.10 GHz \\ Graphics card model & NVIDIA GeForce RTX 3090, RTX (24 GB) \\ programming language & Python 3.10 \\ Deep Learning Framework & Pytorch 2.5.1+cu124 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Configuration of the experimental environment. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**ACC**} & \multicolumn{4}{c}{**F1**} \\ \cline{3-5}  & & **NR** & **FR** & **TR** & **UR** \\ \hline DTC & 0.454 & 0.733 & 0.355 & 0.317 & 0.415 \\ SVM-RBF & 0.318 & 0.318 & 0.318 & 0.318 & 0.318 \\ GRU & 0.646 & 0.792 & 0.574 & 0.608 & 0.592 \\ RvNN & 0.723 & 0.723 & 0.723 & 0.723 & 0.723 \\ PPC\_RNN + CNN & 0.667 & 0.667 & 0.667 & 0.667 & 0.667 \\ Bi-GCN & 0.886 & 0.891 & 0.860 & **0.930** & 0.864 \\ GCN-Bert & 0.872 & 0.853 & 0.892 & 0.823 & **0.911** \\ HAGNN & 0.865 & 0.813 & 0.870 & 0.905 & 0.896 \\ ICP-BGCN & **0.897** & **0.904** & **0.959** & 0.850 & 0.869 \\ \hline \hline \end{tabular} Bold indicates optimal results.  
\end{table} 
Table 3: Comparative experimental results on Twitter15 dataset. As evidenced by the experimental results in Tables 3 and 4, the ICP-BGCN model achieves superior classification performance with accuracies of 89.7% and 91.7% on the Twitter15 and Twitter16 datasets respectively, demonstrating marked superiority over baseline models. Additionally, it exhibits exceptional performance across precision and recall metrics: achieving precision and recall rates of 90.9% and 89.3% on Twitter15, and 90.1% and 91.7% on Twitter16. This performance profile confirms its dual capability in maintaining classification accuracy while ensuring comprehensive sample coverage. Furthermore, ICP-BGCN performs well on the NR, FR, TR and UR criteria on both datasets. The model is able to maintain a high level of performance when dealing with different categories of samples, achieving more than 85% on all criteria. The combined advantages of the multidimensional metrics validate the robustness and stability of the model in handling different categories of rumor detection tasks. Through experiments, it can be found that the detection method based on deep learning is superior to the detection method based on machine learning. On the Twitter15 dataset, the accuracy of ICP-BGCN is 44.3% higher than that of DTC and 57.9% higher than that of SVM-RBF. The main reason is that machine learning relies on manual extraction of features, which needs to rely on the experience and judgment of workers, while deep learning-based models can automatically capture deeper features and the correlation between features, thus better identifying false information. Among the seven deep learning-based misinformation detection models, ICP-BGCN, HAGNN, GCN-Bert, Bi-GCN utilize graph neural networks to extract false information propagation structure features, and demonstrate better performance than the other three models. This shows that GNNs are effective to model the propagation process of information using propagation graphs and extract propagation structure features. Our ICP-BGCN model fuses the propagation structure and the semantic features of the message text. Compared with Bi-GCN, which only considers the structure of information dissemination, GCN-Bert, which utilizes text information features at different granularities, and HAGNN, which captures multi-level semantic information of text content and combines the structural features of dissemination networks, the detection accuracies on the Twitter15 dataset are improved by 1.1%, 2.5%, and 3.2%, respectively.