# Detecting Social Media Bots with Variational AutoEncoder and k-Nearest Neighbor
## 2 Related Work
### Social Media Bots  
Research on social media bot detection has all been relatively recent and is mainly based on the dynamic content sent by social media bots and the social relationship graph around bots. The detection steps include preprocessing collected data, then using the content and behavior information to select some representative and differentiated features. In order to achieve better classification results, most bot detection methods are based on supervised bot learning and involve manual label data. [PERSON] et al. [7] proposed a social botnet detection algorithm based on a trust model, which is used to identify trusted paths in online social networks. The trust accuracy of social bot detection among participants is improved by integrating the trust value of the direct relationship determined by the Bayesian theory and the trust value of the indirect relationship determined by the Dempster-Shafer theory. [PERSON] [8] proposed a learning automata-based malicious social bot detection (LA-MSBD) algorithm integrating a trust computation model with URL-based features for identifying trustworthy participants (users) in the Twitter network. [PERSON] et al. [9] proposed a method to combine the old features to obtain more complex features. [PERSON] et al. [10] detected bots and gender in two languages (English and Spanish). An integrated architecture (AdaBoost) was used to solve the bot detection problem for accounts in English, while a single support vector machine (SVM) was used for accounts in Spanish. The accuracy of the final model in the bot detection task was more than 90%. [PERSON] et al. [11] was the first open interface of social media bot detection on Twitter. The system considered six kinds of features--network, user, dating, time, content, and emotion--and extracted more than 1000 attribute features for analysis to determine whether the user to be detected was a malicious social media bot or a normal user. The method compared random forest (RF), AdaBoost, logistic regression (LR), and decision tree (DT), and the random forest model had the best classification effect with an accuracy rate of 95%.  
In recent years, the deep learning method has become more popular. [PERSON] et al. [12] proposed a deep neural network based on context LSTM, which used content and account metadata to detect bots at the level of the individual tweet. This method extracted context features from user metadata as auxiliary input of a deep network to process tweet text.  
In addition, a technology based on combined minority oversampling (SMOTE, Synthetic Minority Oversampling Technology) was proposed to generate a large label dataset suitable for deep network training from the minimum number of label data (about 3000 complex Twitter bot examples). The experiments indicated that the method achieved high classification accuracy (area under the curve of \(>\)96%) for one tweet in the context of separating bots from humans. Other related studies [13; 14; 15; 16; 17] are similar to these studies; their basic approach is to detect social media bots by extracting distinguishing features.  
In summary, the focus when detecting social media bots is to extract effective distinguishing features and then classify them. In addition to the commonly used classification-based detection methods, there is also a social network structure-based analysis [18], a clustering-based unsupervised detection scheme [19], and a crowdsourcing-based detection method [20]. Although unsupervised detection technology is not as popular as supervised methods, unsupervised methods are suitable for discovering differences between bots and real user groups.