## 5 Experiments and Discussion
### Performance Comparison  
To answer **EI1**, we contrasted TRSA with baseline models on two real datasets. The experimental results are reported in Table 2. The bold values represent the best results, and the underlined values represent the second-best results.  
We can obtain several observations, as follows:  
* The deep neural network models are superior to the models based on feature engineering (DTC, SVM-TS). The most fundamental reason is that deep neural network models can automatically learn implicit high-level semantic representations, whereas traditional machine learning methods that rely on feature engineering can only capture obvious false information in the presentation layer, which leads to various limitations;
* The models that add semantic interactions between claims and comments (DTCA, BERT-Emo) perform better than the models that work with text and hierarchical time-series structure (HSA\(\backslash\)BLSTM). DTCA automatically captures controversial portions of source information through a co-attention mechanism. The BERT-Emo model constructs a dual emotional feature set by measuring the difference between the emotions of an information publisher and their audience to improve false information detection performance;
* The models based on information propagation structure are superior to the models based on text semantics (DTCA, BERT-Emo, HAS-BLSTM). For example, GLAN, BiGCN, and DDGCN achieved improvements of approximately 0.5% to 3.2% in terms of accuracy on the two datasets compared to DTCA. This indicates that mining the hidden structural features of information propagation is very helpful for improving detection performance. However, in terms of precision, because DTCA uses decision trees to filter out some low-credibility noise comments, its performance was approximately 1.5% higher than that of the aforementioned models on PHEME. Moreover, it can be observed that DDGCN showed better performance than BiGCN and  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{4}{c}{**PHEME**} & \multicolumn{4}{c}{**CED**} \\ \cline{2-9}  & **A** & **P** & **R** & **F** & **A** & **P** & **R** & **F1** \\ \hline DTC & 0.669 & 0.678 & 0.678 & 0.667 & 0.731 & 0.731 & 0.719 & 0.725 \\ SVM-TS & 0.722 & 0.788 & 0.758 & 0.721 & 0.857 & 0.859 & 0.858 & 0.859 \\ \hline HSA\(\backslash\)BLSTM & 0.757 & 0.772 & 0.731 & 0.745 & 0.878 & 0.877 & 0.876 & 0.876 \\ DTCA & 0.823 & 0.861 & 0.791 & 0.825 & 0.901 & 0.921 & 0.891 & 0.902 \\ BERT-Emo & 0.800 & 0.795 & 0.795 & 0.793 & 0.905 & 0.916 & 0.913 & 0.914 \\ \hline GLAN & 0.828 & 0.824 & 0.822 & 0.823 & 0.918 & 0.917 & 0.914 & 0.915 \\ BiGCN & 0.847 & 0.840 & 0.834 & 0.835 & 0.919 & 0.918 & 0.916 & 0.917 \\ DDGCN & 0.855 & 0.846 & 0.841 & 0.844 & 0.922 & 0.920 & 0.931 & 0.925 \\ \hline
**TRSA** & **0.885** & **0.896** & **0.871** & **0.881** & **0.953** & **0.950** & **0.954** & **0.952** \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results contrasted between different methods.  
GLAN, indicating that spatiotemporal structure features can finely depict the semantic interaction in message propagation and thus improve performance;
* The proposed model outperformed most post-based models and propagation-based models in terms of most indicators on the two real datasets. Compared to DTCA, the proposed model enriched the claim and comment semantic information from the perspective of time and space propagation structures. Its performance was 5.7%, 3.2%, 7.15%, and 5.3% higher than that of DTCA in terms of accuracy, precision, recall, and F1, respectively. Compared to DDGCN, these four indicators were 3%, 4%, 2.65%, and 3.5% higher on average. This is because DDGCN treated all comments equally, which introduces noise. In contrast, our model reduced noise by calculating the credibility of comments.