# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 4 Experiment, Analysis and Discussion
### Experimental Results and Discussion  
First, we analyze evaluation measures of compared methods. It can be concluded that the effect of the DT-Rank model is poor by analyzing Table 4, because the regular expression used by the model only accounts for 1.6% of Weibo. LK-RBF and SVM-TS extract more microblog posts with more features, so the effect is better. Compared with the \begin{table}
\begin{tabular}{c c} \hline \hline
**Layer** & **Setup** \\ \hline Convolution Layer & One layer; Conv2d(1, 3, (3, 51)) \\ \hline Attention Layer & One layer; Conv2d(2, 3, (3, 51), padding = 0, bias = Flase); \\  & Sigmoid() \\ \hline Pooling Layer & One layer; MaxPool2d((2, 1), 1) \\ \hline Fully Connected Layer & Linear(51, 4) \\ \hline Classify Function & Softmax \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Setup of the CNN with the attention mechanism. DT-Rank algorithm, the accuracy of the LK-RBF model is increased by 3.3%, the accuracy is increased by 3.7% and the recall is improved by 4.0%. The SVM-TS model improves more on these evaluation indicators. This is because the SVM-TS model not only retains the good generalization ability of SVM, but also consider these features that change over time. Similarly, the \(\text{SVM}_{\text{com}}\text{}^{\text{DTS}}\) model uses a dynamic time series algorithm to capture characteristic variables that changed over time. Compared with SVM-TS, the accuracy, precision, recall and F1 are increased by 3.6%, 3.7%, 3.7% and 3.7%, respectively, which proves that the model that considers the time series is more effective. In addition, the \(\text{SVM}_{\text{all}}\text{}^{\text{DTS}}\) model obtained by adding three new features (post popularity, post ambiguity and post spread) shows a roughly 1.7% improvement in evaluation indicators compared with \(\text{SVM}_{\text{com}}\text{}^{\text{DTS}}\). Compared with the traditional machine learning methods above, when the time series features of rumor events are also extracted by the time series algorithm, the rumor recognition effect of model with time series features is stronger than the model without time series features. This proves that adding time series features to the model can effectively detect rumors. The rumor recognition effect of the CNN-TS + Sentiment model is better than the abovementioned traditional machine learning methods. Compared with the DT-Rank, LK-RBF, the accuracy, precision, recall and F1 values of the model have been improved by more than 18%. Although, compared with \(\text{SVM}_{\text{all}}\text{}^{\text{DTS}}\), the model increases the accuracy, recall and F1 values by 1.3%, 5.4%, 1.8%, respectively. Although the improvement is relatively small, our model greatly reduces the input parameters and reduces the number of model training iterations. The input size of \(\text{SVM}_{\text{all}}\text{}^{\text{DTS}}\) is also \(50\times k\), but the input size of our model is \(20\times(k+1)\), and \(k\) is the dimension of input vector. Additionally, \(\text{SVM}_{\text{all}}\text{}^{\text{DTS}}\) needs to manually extract topic features, user features and content features changed over time. It consumes a lot of manpower and time. Therefore, the effect of our model can be considered significant. The CNN-TS + Sentiment model proposed in this paper is based on the CNN-TS model with time-varying sentiment tendency features which increase the accuracy, recall