# Cross-Modal Fake News Detection Method Based on Multi-Level Fusion Without Evidence
## 4 Experiment
### Experimental Setup  
The environment settings for this experiment is shown in Table 5.  
The parameter settings for this experiment is shown in Table 6.  
Where, the values of \(\lambda\) and \(\gamma\) are the optimal parameter combinations determined by the dynamic grid search described in Section 5.4. The remaining parameters are based on our experience in related fields and reference to empirical values from other studies in the field [23;24]. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Label** & **Description** \\ \hline
1 & TP & Number of news stories where the truth value label is fake and the predicted label is fake \\
2 & FP & Number of news stories where the truth value label is real and the predicted label is fake \\
3 & TN & Number of news stories where the truth value label is real and the predicted label is real \\
4 & FN & Number of news stories where the truth value label is fake and the predicted label is real \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Labeling concept descriptions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Experimental Environment** & **Settings** \\ \hline Pytorch & 1.11.0 \\ Python & 3.8 \\ Cuda & 11.3 \\ GPU & RTX 4090 (24 GB) \\ CPU & 22 vCPU AMD EPYC 7T83 64-Core Processor \\ Memory & 90 GB \\ Hard Disk & 30 GB + 50 GB \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental environment settings. The experimental results from the Weibo dataset show that CM-MLF performs best among all baseline models. Specifically, CM-MLF outperforms the best-performing AGMFN model by 0.5% in terms of accuracy, achieving an accuracy of 92.2%. In addition, CM-MLF generally outperforms other models in other key metrics such as precision, recall, and F1 score. Among them, the CM-MLF model can achieve the highest precision rate of 93.5% for fake news detection. These results validate the effectiveness and robustness of this paper's model in handling multimodal data and detecting fake news. The experimental results on the Twitter dataset show that CM-MLF model still performs the best among all baseline models. Specifically, CM-MLF outperforms the best-performing AGMFN model by 5.4% in terms of accuracy, reaching 90.1% accuracy. Furthermore, CM-MLF generally outperforms other models in other key metrics such as precision, recall, and F1 score. Among them, the CM-MLF model can achieve the highest precision rate of 99.0% for true news detection. In addition, these models perform better overall on the Weibo dataset. This is because the two datasets themselves have certain differences. For example, the average length of a news article on the Weibo dataset is about 10 times that on the Twitter dataset. Figure 5: Bar chart of comparative experimental results on the Twitter dataset. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**Ac**} & \multicolumn{3}{c}{**Rumor**} & \multicolumn{3}{c}{**Non-Rumor**} \\ \cline{3-8}  & & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Att\_RNN & 0.681 & 0.758 & 0.659 & 0.705 & 0.603 & 0.712 & 0.653 \\ EANN & 0.678 & 0.765 & 0.641 & 0.698 & 0.597 & 0.729 & 0.657 \\ MVAE & 0.598 & 0.697 & 0.543 & 0.610 & 0.518 & 0.676 & 0.587 \\ SAFE & 0.643 & 0.676 & 0.506 & 0.579 & 0.625 & 0.772 & 0.691 \\ CARMN & 0.735 & 0.778 & 0.652 & 0.709 & 0.704 & 0.817 & 0.756 \\ CAFE & 0.806 & 0.807 & 0.799 & 0.803 & 0.805 & 0.813 & 0.809 \\ BMR & 0.842 & 0.821 & 0.782 & 0.796 & 0.845 & 0.904 & 0.891 \\ CSFND & 0.833 & 0.899 & 0.799 & 0.846 & 0.763 & 0.878 & 0.817 \\ AGMFN & 0.847 & 0.816 & 0.913 & 0.883 & 0.933 & 0.805 & 0.803 \\
**Ours** & **0.901** & 0.816 & **0.988** & **0.894** & **0.990** & 0.838 & **0.908** \\ \hline \end{tabular} The bold texts in the table highlight the advantages of CM-MLF model.  
\end{table} 
Table 8: Comparative experimental results on twitter datasets. Att-RNN struggles with complex modal interactions due to its single-attention fusion mechanism and the temporal limitations of LSTM.