# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 2 Related Works
### Method Based on Deep Learning  
In recent years, deep neural networks have become more and more popular and they have achieved good performance in many NLP tasks. [PERSON] et al. [20] used multiple RNNs to deeply mine dynamic time features and modeled the social background information of events as N equal time series, so as to obtain text sequence coding and capture the background information of relevant posts over time. On this basis, [PERSON] et al. [21] divided the whole rumor recognition model into three modules: Capture, Score and Integrate, in which the Capture module uses RNN to learn the time representation of the text. From the experimental results, their experimental results are better than previous work (SVM-TS [18], \(\text{SVM}_{\text{com}}\text{}^{\text{DTS}}\)[19]) which prove the effectiveness of deep neural network model in rumor detection. The RNN was used to learn text content of rumors shows that the length of time series is equal to the number of posts and the calculation is too large. Later scholars used GRU [8] and its variant gated neural network model (PGNN) [22] to track the time and broadcast features of posts. Additionally, then they added attention mechanism to adjust the weight of each node which achieved better performance. However, the models adopted by these scholars only considered the dynamic time features of post propagation. They did not mine the deep text features inside the posts. [PERSON] et al. [23] proposed a convolution-long-short memory network (C-LSTM) model based on the historical off-linefeatures of microblog users. The model combines the time feature and the internal feature of the post, but this is more complex. The number of input parameters is large and this model occupied a large number of resources. Some scholars have also improved the LSTM model [24; 25] by combining word attention and context information, or mining the homogeneity and dialogue structure features of rumors which has achieved good results. However, the relationship between the different life cycles and the life cycle of the development of microblog events has not been considered. Additionally, the model is complex. In addition, the propagation structure of rumors has also been proven to be effective in identifying rumors. [PERSON] et al. [26] proposed a bi-directional graph convolution network (Bi-GCN) model to explore the two structural features of rumor propagation and diffusion by manipulating the top-down and bottom-up rumor propagation. Although the accuracy of the model reached 88.6% on the experimental data set, it has several GCN layers and the size of the input data set is the number of posts. The calculation of the whole model is relatively expensive. [PERSON] et al. [27] and [PERSON] et al. [28] proposed a tree-like neural network model to learn the propagation features of rumors by tracking the non-sequential propagation structure of tweets or representing social media conversations as binary constituency trees. In rumor recognition, the F1-macro value of this model is higher than that of the best model at present which proves that the features of communication structure can effectively identify rumors. However, in this changing society, the propagation structure of rumors will also change. When the model that has learned the features of propagation structure faces new rumors, the effect may be greatly reduced. The convolutional neural network model has produced good results in rumor recognition. [PERSON] et al. [29] and [PERSON] et al. [30] used the convolutional neural network (CNN) model to train and mine the internal text features of rumors. However, they did not take into account the time series features in the life cycle of rumors. The graph convolution neural network [31; 32] derived from the convolution neural network and the improved model EGCN [33] convert the microblog rumor data to graph data. Then, they use the convolution neural network to train the labeled data.