# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 2 Related Works
### Method Based on Deep Learning
By updating the node weight in the graph, the information is passed to the unlabeled data which greatly reduces the workload of rumor data annotation. The variants of these CNN models have achieved good results in rumor recognition. However, the number of parameters of these models is the product of the output dimensions of the two hidden layers, Therefore, the calculation is expensive. [PERSON] et al. [29] proposed the basic CNN model and used Doc2 Vec [34] to vectorize microblog posts and set the dimension size of the convolution kernel as the length of the post vector. These operations can effectively extract features between posts, and they also can extract features within the posts. The model greatly reduces the labor intensity of artificial feature construction and learns the deep features between microblog posts. However, they do not consider the time series features of posts between different life cycles of rumors. Based on this, [PERSON] et al. [10] improved the model by using the time series algorithm (DTS) to divide the input of the model into 20 groups according to the time series. The model considers the time series features of rumor development and the parameters are reduced from 59,138 \(\times\) 50 to 20 \(\times\) 50 which greatly reduces the amount of calculation. However, the sentiment polarity of the event is not considered, and the key input features are not highlighted, so the accuracy of model needs to be improved. Compared with delicate sentiment features, sentiment polarity features are more stable in complex environments. [PERSON] et al. [35] and [PERSON] et al. [36] extracted 13 features such as positive and negative words of tweets, the length of tweets and sentimental features. The CNN and TF-IDF models were used to classify rumors. The precision rate, recall rate and F1 value of these two experiments increased by about 4%, which proved that the sentiment polarity feature could effectively identify rumors. The novel two-layer GRU model proposed by [PERSON] et al. [37] extracted the fineness features of rumor events and verified it on the OSNs data set. The results showed that it was superior to the latest rumor detection model [38]. However, this scholar used a new sentiment dictionary to capture the features of delicacy which has a large amount of manpower. With the changing of the social network environment, the sentiment features of delicacy are sometimes inferior to the sentiment polarity features, because, in the face of different types of events, the emotions reflected in the comments of netizens are different. For example, most Internet rumors about international security are satire and criticism; when the events are about the death of a well-known person, they are sad; when the events are about natural disasters in society, they are sympathetic. Additionally, the models with counting sentiment features are highly complex and they require large amounts of calculation. Enhanced feature representation plays a positive role in rumor recognition. On the one hand, multi-layer GCN [39] can enhance short-term information and data representation, then it can effectively deal with the early content of rumors [40]. On the other hand, the method is by adding an attention mechanism to the model. [PERSON] et al. [41] and [PERSON] et al. [42] add an attention mechanism to convolutional neural networks to emphasize the weight of uncertain detection factor features and measure feedback mechanisms. [PERSON] et al. [43] and [PERSON] et al. [44] proposed a multi-task learning framework to detect rumors. The model was divided into a shared layer and a task-specific layer. An attention mechanism was introduced in the rumor detection process to mine the subtle semantic features hidden in it. The former was used to add image features published with rumor posts and the latter was used to add user credibility features.