# Research on Dual-Emotion Feature Fusion and Performance Improvement in Rumor Detection
## 4 Experiments and Results
### Experimental Results
#### 4.3.3 Experimental Results of Emotional Characteristics in the Ablation Experiment  
In this experiment, the data in Weibo-16 were selected to be re-split according to a time series. The latest 20% of the data were selected as the test set. Of the remaining 80% of the new dataset, the latest 25% was selected for validation, and the rest of the data were used as the training set. In this way, model training can avoid over-fitting and adapt to the update and development of network rumors to the greatest extent.  
As can be seen in Figure 9, the F1 value reached 86.7% after adding dual-emotion features to the CNN detector, and the enhancement effect of social emotions and the emotion gap was significantly better than that of publisher's emotional features. As shown in Figure 10, although the fluctuation in the data is relatively smooth, the detection performance of dual-emotion features is still the best, and the enhancement effect of publisher's emotional features on rumor detection is low.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Macro F1 Score**} & \multirow{2}{*}{**Precision**} & \multicolumn{2}{c}{**F1 Score**} \\ \cline{3-5}  & & & **Rumor** & **Non-Rumor** \\ \hline BiGRU & 0.807 & 0.822 & 0.754 & 0.860 \\ +Emoratio & 0.794 & 0.810 & 0.738 & 0.851 \\ +EmoCred & 0.766 & 0.778 & 0.711 & 0.820 \\ +Dual-emotion features & **0.826** & **0.838** & **0.781** & **0.871** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of the BiGRU model.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Macro F1 Score**} & \multirow{2}{*}{**Precision**} & \multicolumn{2}{c}{**F1 Score**} \\ \cline{3-5}  & & & **Rumor** & **Non-Rumor** \\ \hline CNN & 0.824 & 0.845 & 0.762 & 0.886 \\ +Emoratio & 0.837 & 0.857 & 0.780 & 0.894 \\ +EmoCred & 0.849 & 0.867 & 0.797 & 0.901 \\ +Dual-emotion features & **0.871** & **0.883** & **0.832** & **0.911** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Results of the CNN model.  
Figure 9: Detection results of CNN model on Weibo-16 and Weibo-16 (t).  
The results of these two models on the two datasets, Weibo-16 and Weibo-16 (t), were comprehensively analyzed. The three components of the dual-emotion features can be used independently in any model to enhance the effect; the improvement of the macro F1 score by supplementing the data with social-emotional features or the emotion gap is greater than that of the publisher's emotional features, and the performance of social emotions and the emotion gap is relatively consistent. Therefore, among the three emotional characteristics, social emotion and the emotion gap can play a greater role in detection. It is found that, compared with social comments, there are fewer emotional resources available to extract the emotions of publishers, which leads to the insufficient analysis of texts. However, the comment base of rumors is large, which provides sufficient emotional resources for the extraction of emotional features.