## 7 Results
### Fake News Detection Performance
Summaries of the prediction results of the models in the PolitiFact and GossipCop datasets are shown in the forms of confusion matrices in Figures 12-14 and Figures 15-17, respectively. Figures 18 and 19 show the overall scores across all of the models in both datasets, respectively. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline SAF [19] & 0.691 & 0.638 & 0.789 & 0.706 \\ \hline BiLSTM-BERT [36] & 0.885 & NA & NA & NA \\ \hline LNN-KG [37] & 0.880 & 0.9011 & 0.880 & 0.8892 \\ \hline Logistic Regression (N-Gram) [38] & 0.80 & 0.79 & 0.78 & 0.78 \\ \hline dEFEND [20] & 0.904 & 0.902 & 0.956 & 0.928 \\ \hline BERT\({}_{\text{base}}\)-CNN-BiGRU-ATT & 0.9281 & 0.9375 & 0.9375 & 0.9375 \\ \hline BERT\({}_{\text{base}}\)-BiGRU-CNN-ATT & 0.8705 & 0.8690 & 0.9125 & 0.8902 \\ \hline BERT\({}_{\text{base}}\)-CNN-BiGRU & 0.9137 & 0.9359 & 0.9125 & 0.9241 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison (%) of the results obtained in the PolitiFact dataset using news content and user posting behavior features. Figure 4: Confusion matrix of BERT\({}_{base}\)-CNN-BiGRU-ATT in PolitiFact. Figure 5: Confusion matrix of BERT\({}_{base}\)-BiGRU-CNN-ATT in PolitiFact. Figure 8: Confusion matrix of BERT\({}_{base}\)-CNN-BiGRU-ATT in GossipCop. Figure 6: Confusion Matrix of BERT\({}_{base}\)-CNN-BiGRU in PolitiFact. Figure 7: Performance comparisons (%) of all models in PolitiFact. Figure 11: Performance comparisons (%) of all models in GossipCop. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline SAF [19] & 0.691 & 0.638 & 0.789 & 0.706 \\ \hline BiLSTM-BERT [36] & 0.885 & NA & NA & NA \\ \hline LNN-KG [37] & 0.880 & 0.9011 & 0.880 & 0.8892 \\ \hline Logistic Regression (N-Gram) [38] & 0.80 & 0.79 & 0.78 & 0.78 \\ \hline dEFEND [20] & 0.904 & 0.902 & 0.956 & 0.928 \\ \hline BERT\({}_{\text{base}}\)-CNN-BiGRU-ATT & 0.8777 & 0.8621 & 0.9375 & 0.8982 \\ \hline BERT\({}_{\text{base}}\)-BiGRU-CNN-ATT & 0.8849 & 0.8644 & 0.8644 & 0.8644 \\ \hline BERT\({}_{\text{base}}\)-CNN-BiGRU & 0.8993 & 0.9342 & 0.8875 & 0.9103 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison (%) of the results obtained in the PolitiFact dataset w/o user posting behavior features. Figure 10: Confusion matrix of BERT\({}_{\text{base}}\)-CNN-BiGRU in GossipCop. Figure 9: Confusion Matrix of BERT\({}_{\text{base}}\)-BiGRU-CNN-ATT in GossipCop. Figure 14: Confusion matrix of BERT\({}_{base}\)-CNN-BiGRU in PolitiFact w/o user posting behavior features. Figure 12: Confusion matrix of BERT\({}_{base}\)-CNN-BiGRU-ATT in PolitiFact w/o user posting behavior features. Figure 13: Confusion matrix of BERT\({}_{base}\)-BiGRU-CNN-ATT in PolitiFact w/o user posting behavior features. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Model** & **Accuracy (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** \\ \hline SAF [19] & 0.796 & 0.820 & 0.753 & 0.785 \\ \hline Logistic Regression (N-Gram) [38] & 0.82 & 0.75 & 0.79 & 0.77 \\ \hline dEFEND [20] & 0.808 & 0.729 & 0.782 & 0.755 \\ \hline BERT\({}_{\text{base}}\)-CNN-BiGRU-ATT & 0.8584 & 0.8849 & 0.9361 & 0.9098 \\ \hline BERT\({}_{\text{base}}\)-BiGRU-CNN-ATT & 0.8501 & 0.8902 & 0.9165 & 0.9031 \\ \hline BERT\({}_{\text{base}}\)-CNN-BiGRU & 0.8619 & 0.8619 & 0.9751 & 0.9150 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison (%) of the results obtained in the GossipCop dataset w/o user posting behavior features. Figure 16: Confusion matrix of BERT\({}_{\text{base}}\)-BiGRU-CNN-ATT in GossipCop w/o user posting behavior features. Figure 15: Confusion matrix of BERT\({}_{\text{base}}\)-CNN-BiGRU-ATT in GossipCop w/o user posting behavior features. Based on the experimental results, we made the following observations. 1.