## 4 Experiments
### Experiment Settings  
To confirm the efficacy of the model posited in this paper for the task of disinformation detection, this study contrasts it with other foundational techniques within the experimental framework. The methodological specifics are elucidated as follows:  
DTC [41]: A classification model based on decision trees, which manually extracts features to obtain information credibility for false information detection.  
SVM-RBF [6]: The model is based on a support vector machine with an RBF kernel, which utilizes the aggregate statistics of the postings for disinformation detection features by manually constructing features.  
GRU [8]: A deep learning model based on RNN that detects false information by learning the propagation sequence of messages, i.e., the temporal structure characteristics of events.  
RvNN [42]: A false information detection method based on a tree-structured recurrent neural network with GRU units.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistics** & **Twitter15** & **Twitter16** & **Pheme** & **SemEval-17** \\ \hline Number of events & 1490 & 818 & 5473 & 325 \\ Non-rumor & 374 & 205 & - & - \\ False rumors & 370 & 205 & 3477 & 74 \\ Unsubstantiated rumor & 374 & 203 & - & 106 \\ True rumor & 372 & 205 & 1996 & 145 \\ Number of users & 276,663 & 173,487 & 36,443 & - \\ Average number of posts & 223 & 251 & 20 & - \\ Maximum number of posts & 1768 & 2765 & 346 & - \\ Minimum number of posts & 55 & 81 & 3 & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of the dataset.  
PPC_RNN + CNN [43]: The model combines RNN and CNN to learn the representation of events through the user information on the message propagation path, and then identifies false information.  
Bi-GCN [11]: This is a graph model using a bidirectional graph convolutional neural network. Features are extracted from top-down (top-down) and bottom-up (bottom-up) propagation directions of rumors for detection.  
GCN-Bert [44]: The rumor detection method not only considers the features of the message itself, but also utilizes the rumor features of all relevant texts and words.  
HAGNN [45]: A graph neural network-based disinformation detection model that captures high-level representations of textual content at different granularities, and fuses propagation structures for disinformation detection.  
The experiment detailed in this paper is executed on the Ubuntu 22.04 platform, with the experimental environment consisting of Python 3.10 and PyTorch 2.1.0. Table 2 presents the precise specifications of the experimental setup. To ensure impartiality in comparison, the dataset was randomly partitioned into five segments and a five-fold cross-validation experiment was conducted on these segments. During training, the configurations were set as follows: hidden layer dimension to 64, iterations (epoch) to 200, batch size (batch_size) to 128, learning rate to 0.0005, and dropout rate to 0.2. The Adam optimization algorithm facilitated model parameter updates, while the early stopping technique was implemented to prematurely cease training if the validation set's loss value remained stagnant for 10 consecutive trials. Model efficacy was gauged using the evaluation metrics: Accuracy (Acc) and F1 score.