# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 3 Problem Statement and Proposed Solution
### Time Series Grouping and Extraction of Sentimental Characteristics  
For Time Series Grouping operations, [PERSON] [10] pointed out that the number of posts for an influential Weibo event is at least a few hundred, or even tens of thousands. Additionally, the number of posts for different events varies greatly. However, the life cycle of a rumor event is divided into the incubation period, the breeding period, the spreading period and the fading period. The text features of Weibo posts in these four time periods and between the four time periods have similar features and changing trends. Therefore, these adjacent posts arranged in time series are regarded as a group, representing a specific life cycle of the event. The consideration for this is: pay more attention to the feature relationship of the content of Weibo posts in each time period so that it can extract the features of the Weibo text between each time period, rather than the feature relationship between a single Weibo post. The development of a Weibo rumor event will have different post content in various time periods. Additionally, this can greatly reduce the complexity of the model input data. The time series segmentation of posts refers to the operation in J. MA [9]. For an event \(E_{i}\), the start time of the post is \(time\_Begin_{i}\), the end time of the post is \(time\_End_{i}\). Each time \(t_{i,j}\) of microblog \(m_{i,j}\) is converted to a timestamp between 0 and \(N,N\) is the number of time intervals, in this experiment \(N\) is taken as 20. \(N=20\) refers to the experimental setup of [PERSON] et al. [10]. \(time\_Stamp_{mi,j}\) is the index of time stamp which \(m_{ij}\) falls into, taking the value of 0, 1, \(\ldots,\,N\). Then, the calculation Equations for  
Figure 2: Two training methods of the Doc2 Vec model. the _time_Interval_i_ of the post _time_Interval_i_ of the event \(E_{i}\) and the _time_Stamp_mi,j_ of the timestamp of each post are shown in Equations (4) and (5). \[time\_Interval_{i}=\left\lfloor\frac{time\_End_{i}-time\_Begin_{i}}{N}\right\rfloor \tag{4}\]  
\[time\_Stamp_{m_{i,j}}=\left\lfloor\frac{t_{i,j}-time\_Begin_{i}}{time\_Interval_{i}}\right\rfloor \tag{5}\]  
Then, we collect the timestamps of all related Weibo posts and subtract the start timestamp of the corresponding event from all the timestamps of each event. Then, we normalize these timestamps to a 0-1 scale. Finally, the entire timestamp is equally divided into 20 shares in chronological order and the posts in each time window are expressed as shown in Equation (6). \[T_{i}=\left(time\_Stamp_{m_{i,j-1}},time\_Stamp_{m_{i,j}}\right)\qquad j=1,2,3, \ldots,20 \tag{6}\]  
Then, the grouped posts \(T_{i}\) are vectorized with Doc2 Vec text and passed into the convolutional neural network training as the input matrix. After the related posts of the \(i\)-th Weibo rumor event are grouped, the expression of the vector matrix obtained is shown in Equation (7). \[V(E_{i})=(F_{i,1},F_{i,2},\ldots,F_{i,20}) \tag{7}\]  
In which \(E_{i}\) is the \(i\)-th rumor event and \(F_{i,20}\) is the grouped 20 feature vectors. For extracting sentimental features, we select the SVM model to analyze the polarity of the rumor/non-rumor data set. The overall process is shown in Figure 3. First of all, this paper selects the Weibo data set with sentiment polarity tags. Additionally, then we count the number of positive and negative words in the data set. we use chi-square statistics technology to extract the chi-square features of the text. Then, a Constructing SVM classification is used to obtain the sentiment feature word vector. The chi-square statistic calculation Equations are Equations (8) and (9). \[\chi^{2}(w,s)=\frac{N[p(s,w)p(\overline{s},\overline{w})-p(\overline{s},w)p(s,\overline{w})]}{f(s,w)} \tag{8}\]  
\[ \begin{array}{l}f(s,w)=[p(s,w)+p(s,\overline{w})][p(s,w)+p(\overline{s},w)] \times\\ [p(\overline{s},w)+p(\overline{s},\overline{w})][p(s,\overline{w})+p(\overline {s},\overline{w})]\end{array}