# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 1 Introduction  
By the end of 2020, China had 989 million netizens and 70.4% Internet penetration, with more than 1.6 billion mobile Internet users. With the popularity of the Internet, microblog, WeChat and other applications gradually occupy people's lives. They become important platforms for publishing and collecting information. Taking Weibo as an example, on the one hand, Sina Weibo provides sentiment outlets for the public. On the other hand, it also forms a hotbed for making and disseminating rumors [1]. Rumors on social media are rampant and it is difficult to distinguish between credible and untrustworthy information, which can lead to social unrest and seriously endanger national security. Therefore, it is particularly important to detect rumors in the early development of rumors. Rumor recognition has always concerned scholars. The existing rumor recognition models based on machine learning mainly deal with the content features, link features, character features and keyword features of a large number of microblog rumor texts manually. Then, these models comprehensively utilize user features and interactive features. The SVM model [2; 3; 4], Bayesian classifier [5] and improved Naive Bayes method [6] are used to identify rumors. In addition, deep text sentiment features [7] have been proven to be more effective in identifying rumors than other features. [PERSON] et al. [8] used the construction of sentiment dictionary to study the influence of the delicate emotion of events on rumor recognition. However, for the construction of sentiment dictionary, the manual work is heavy, and the accuracy of the sentiment dictionary also affects the identification of rumors. In fact, in the whole life cycle of rumors, the sentiment polarity of events is also changing, which is different from non-rumors. Moreover, in order to mine the key features of dynamic rumors and enhance robustness, deep neural networks are favored by scholars. [PERSON] et al. [9] used classical RNN to capture the continuity of microblog posts over time. Additionally, the sequence length was equal to the number of posts, which was expensive, ineffective and it did not learn thedeep features of the text. [PERSON] et al. [10] used a time series algorithm to divide the whole rumor life cycle into 20 groups. At the same time, a CNN was used to learn the internal features between different life cycles and within different life cycles. At this time, the length of the input sequence is 20, which greatly reduces the amount of calculation. It also can reduce the resource occupancy rate and improve the accuracy of rumor recognition. However, the sentiment tendencies of posts are different in different life cycles of rumors. [PERSON] et al. did not consider this feature and did not highlight the weight of each feature in different time series. In order to solve the above two problems, we first use the time series (TS) algorithm to divide the time series data of events and then use the classical SVM model to obtain the sentiment polarity in different periods. Finally, the sentiment polarity is combined with the text vector and the fused event features are used as the input of the convolutional neural network for rumor recognition. At the same time, the spatial attention mechanism is added to the model to adjust the weight, so as to focus on more important input features and enhance the effect of rumor recognition. The contributions of this study are as follows:  
1. We verify that sentiment polarity features in different time series can effectively identify rumors. 2. We verify that adding an attention mechanism can effectively adjust the weight of rumor features in different time series, focus on more important features and reduce the number of iterations for model training greatly. 3. We realized the convolutional neural network model with the attention mechanism added by combining sentiment polarity features and time series information.