# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 4 Experiment, Analysis and Discussion  
### Experimental Setup  
Regarding the datasets of the experiment, we mainly use two datasets. The statistics of the two datasets are shown in Table 1.  
**Dataset1:** This experiment used a Weibo data set with sentiment labels collected on the Internet ([[https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/weibo_senti_100k/intro.ipynb](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/weibo_senti_100k/intro.ipynb)]([https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/weibo_senti_100k/intro.ipynb](https://github.com/SophonPlus/ChineseNlpCorpus/blob/master/datasets/weibo_senti_100k/intro.ipynb)) (accessed on 2 April 2018)). This data set can be used for sentiment, opinion and comment tendency analysis. In this experiment, it is used to train the SVM model to be able to detect the sentiment polarity of Dataset2. **Dataset2:** This data set is the public data in the literature of [PERSON] [9]. This data set is based on rumor and non-rumors incident obtained from the Sina community management. The center reported various false information on Sina Weibo. In this data set, the original post of the event and all related forwarding/replying information are included. The Accuracy, Precision, Recall and F1 are usually used to evaluate the effect of the experiment. This paper also selects these four indicators. The specific calculation formula is shown in Equation (16). The evaluating indicators are defined in Table 2.  
\[ \begin{array}{l}accuracy=\frac{TP+TN}{TP+TP+TN+FN}\\ precision=\frac{TP}{TP+NP}\\ recall=\frac{TP}{TP+FN}\\ F1=\frac{2*precision+recall}{precision+recall}\end{array}  \tag{16}\]  
The Accuracy reflects the accuracy of the detection method, and the Recall reflects the coverage rate of the detection method. The larger the values of these two indicators are, the better the overall effect of the detection method is. We compare and verify the CNN-TS-Sentiment-Attention (CTSA) model proposed in this paper with the Weibo rumor detection model proposed by previous scholars which are based on the traditional ML algorithm DT-Rank (2015), SVM-TS (2015), LK-RBF (2016), SVM\({}_{\text{com}}\)DTS (2018), SVM\({}_{\text{all}}\)DTS (2018) and the DNN-based methods of GRU-2 (2016), CNN (2017), CallAtRumors (2018), CNN-TS (2021), CNN-TS\({}^{\text{SVM}}\) (2021). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Data Set** & **Statistical Type** & **Statistical Value** \\ \hline \multirow{4}{*}{Dataset1} & Total Comments & 119,988 \\  & Total Positive Comments & 59,993 \\  & Total Negative Comments & 59,995 \\ \hline \multirow{8}{*}{Dataset2} & Involved Users & 2,746,818 \\  & Total Posts & 3,805,656 \\  & Total Events & 4664 \\  & Total Rumor Events & 2313 \\  & Total Non-Rumor Events & 2351 \\  & Average Posts per Event & 816 \\  & Minimum Posts per Event & 10 \\  & Maximum Posts per Event & 59,138 \\  & Average Time Length per & 2460.7 h \\ \multicolumn{3}{c}{Event} \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Statistics of the rumor events dataset and sentiment polarity data. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Positive** & **Negative** \\ \hline True & True Positive (TP) & True Negative (TN) \\ \hline False & False Positive (FP) & False Negative (FN) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Definition of evaluating indicators. DT-Rank [46]: [PERSON]. [PERSON] proposed a technique based on search query phrases and clustering similar posts together. Then, [PERSON] collected related posts that do not contain these simple phrases. This model has achieved good performance for rumor detection. SVM-TS [18]: [PERSON] used time series to capture the changes in social background features of rumors over time and then used SVM algorithm to detect rumors. LK-RBF [47]: [PERSON] proposed two methods based on label and network link dialogue to solve the problem of implicit data. This paper chooses the link-based method with Radial Basic Function (RBF) which achieves the best results in rumor detection.