# Rumor Detection Based on Attention CNN and Time Series of Context Information
## 2 Related Works
### Method Based on Traditional Machine Learning  
The traditional machine learning methods for rumor recognition are mainly reflected in feature extraction and algorithm improvement. [PERSON] et al. [11] analyzed the explosiveness of Twitter events, post forwarding rate and vocabulary distribution differences to calculate the credibility score. [PERSON] et al. [12] used user features, content features and event features to calculate user credibility, content credibility and event credibility, respectively. Additionally, then they used these features to calculate the Gini coefficient to identify rumors. Recently, scholars still use the minimum credibility to study rumor clarification. The scholar proposed the Longest-Effective-Hops (LEH) algorithm to solve the problem of finding users with the minimum credibility in the shortest time [13]. The extraction of these features can effectively identify rumors, but the features of post forwarding rate and event features depend on a large number of manual collections. Considering the post forwarding situation is dynamic, it is difficult to extract these context features. Manually extracted rumor features cannot adapt to the complex environment of social development and change. [PERSON] et al. [14] comprehensively evaluated the recognition effect of basic models such as OneR (One Rule), Naive Bayes, ZeroR, JRip, Random Forest, Sequential Minimal Optimization and Hoeffding Tree. It is not enough to mine the shallow features related to rumor posts, so [PERSON] et al. [7], [PERSON] et al. [15], [PERSON] et al. [16] and [PERSON] et al. [17] extracted the sentiment features of rumor texts and used random forest model to achieve good rumor recognition results. These verified that the sentiment features of rumor text can effectively identify rumors. However, these scholars only considered the static text features and sentiment features of rumors but did not consider that these features changed with the development of rumors. Therefore, many scholars had paid attention to the time series features of rumors. [PERSON] et al. [18] proposed SVM-TS model to identify rumors. This method used time series modeling technology to interpret varied social background information (post content information, user information, dissemination information), so as to capture the time-varying characteristics of these features. Finally, an SVM model was used to identify rumors. [PERSON] et al. [19] constructed the \(\text{SVM}_{\text{com}}\text{}^{\text{DTS}}\) model to capture the dynamic sequence features of rumors changing with time. On this basis, three new features based on the communication theory of rumor events in sociology (post popularity, post ambiguity and post spread) are added to the \(\text{SVM}_{\text{all}}\text{}^{\text{DTS}}\) model. However, these two scholars only considered the dynamic sequence features of rumor texts changing with time. They also did not consider the deep features of texts and the sentiment features of events in different time series. All in all, the traditional machine learning method has a good effect on rumor recognition. However, on the one hand, although the machine learning method considers the rumor text features, user features, sentiment features and time series, they do not consider the deep features of the text changes with time and, furthermore, it is not adapted to the rumors of the times. On the other hand, the feature engineering of machine learning methods consumes a lot of manpower and material resources. The user features, content features, event features and time series features used in the above traditional machine learning method all need to be constructed manually. For example, these features are manually extracted including the number of users followed, the number of users following, user registration time, special symbols like \"?\" \"!\" and post text content. Then, they are used by classic or improved traditional machine learning models to train and recognize rumor. There is no doubt that such structural features are labor intensive.