# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 4 Experiment, Analysis and Discussion
### Experimental Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Method** & **Accuracy** & **Precision** & **Recall** & **F1** \\ \hline \multirow{6}{*}{Traditional ML-based} & DT-Rank(2015) & 0.648 & 0.649 & 0.649 & 0.648 \\ \cline{2-6}  & LK-RBF(2016) & 0.681 & 0.686 & 0.689 & 0.626 \\ \cline{2-6}  & SVM-TS(2015) & 0.796 & 0.797 & 0.796 & 0.796 \\ \cline{2-6}  & \(\text{SVM}_{\text{com}}\text{}^{\text{DTS}}\)(2018) & 0.832 & 0.834 & 0.833 & 0.833 \\ \cline{2-6}  & \(\text{SVM}_{\text{all}}\text{}^{\text{DTS}}\)(2018) & 0.849 & 0.850 & 0.849 & 0.849 \\ \hline \multirow{6}{*}{DNN-based} & GRU-2(2016) & 0.833 & 0.833 & 0.834 & 0.833 \\ \cline{2-6}  & CNN(2017) & 0.844 & 0.847 & 0.844 & 0.844 \\ \cline{2-6}  & CallAtRumors(2018) & 0.866 & 0.867 & 0.866 & 0.867 \\ \cline{2-6}  & CNN-TS(2021) & 0.851 & 0.839 & 0.866 & 0.852 \\ \cline{2-6}  & CNN-TS\({}^{\text{SVM}}\)(2021) & 0.866 & 0.868 & 0.860 & 0.863 \\ \cline{2-6}  & CNN-TS + Sentiment(ours) & 0.862 & 0.834 & 0.903 & 0.867 \\ \cline{2-6}  & CNN-TS + Sentiment + Attention(ours) & 0.867 & 0.833 & 0.916 & 0.872 \\ \cline{2-6}  & CNN-TSSVM + Sentiment + Attention(ours) & 0.882 & 0.869 & 0.894 & 0.882 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of rumor event detection methods on Sina Weibo. and F1 values by 1.1%, 3.7% and 1.5%, respectively. This proves that considering changes in sentiment polarity features in different life cycles (incubation period, breeding period, spreading period and fading period) is effective for rumor detection. In addition, the attention mechanism is added on this basis to obtain different importance levels (weights) of the input feature matrix and the CNN-TS + Sentiment + Attention model is obtained. The accuracy rate and other evaluation indicators are roughly increased by 1.5-5%. Compared with the CNN-TS model, CNN-TS + Sentiment has improved the accuracy, recall and F1 values. It is proven that the sentiment polarity features in different time series have a positive effect on rumor recognition. Moreover, the input size of the CNN-TS + Sentiment model is 20 \(\times\) 51 and the input size of the CNN-TS model is 20 \(\times\) 50. Only by adding 20 input data can the accuracy and other indicators be increased by more than 1.5 %, which is feasible. Similarly, the CNN-TS + Sentiment + Attention model is compared with the CNN-TS model and the CNN-TS + Sentiment model. The four evaluation indexes are also improved. This proves that adding a simple spatial attention mechanism can enhance the input features and improve the rumor recognition effect. In order to fully prove the positive effect of sentiment polarity features and the attention mechanism in different time series on rumor recognition, we apply these two operations to the CNN-TSSVM model. Compared with the CNN-TSSVM model, accuracy, precision, recall and F1 value are improved by more than 2%. The experimental results fully prove that the application of sentiment polarity features and the attention mechanism in the CNN and TS algorithms can effectively identify rumors. Second, we analyze the parameters of the compared methods. It can be seen from Figure 5 that the input of the CNN [29] is all the posts of a rumor or non-rumor event. If there are \(n\) posts and the encoding dimension of the text vector is \(k\), then the input size of the CNN model is \(n\times k\). The input of CNN-TS [10] is the post grouped by the time series algorithm. \(n\) posts are divided into m groups and the coding dimension of the text vector is still \(k\), so the input size of the CNN-TS model is \(m\times k\). This greatly reduces the input parameters. Our CNN-TS + sentiment + Attention model is improved on the basis of the CNN-TS model and \(n\) posts are still divided into m groups. The feature coding of posts has a \(k\)-dimensional text vector and a sentiment polarity feature. Then, the vector feature dimension of posts is \(k+1\) and the input dimension of CNN-TS + sentiment + Attention is \(m\times(k+1)\).