## 2 Related Works
### Spam Detection Approaches  
At present, spam detection is one of the most important challenges for online social network security. Various types of spam detection methods exist, including crowdsourcing technology, graph-based techniques, and machine learning techniques. Among these, machine learning currently plays an important role in spam detection in social networks. Supervised machine learning algorithms are the most common methods used for spam detection. [PERSON] et al. [8] trained six different classifiers using content, behavior, and network structure features and, then, compared their classification performances. [PERSON] et al. [18] first labeled samples as spam or non-spam and, then, proposed a classification algorithm based on support vector machines to detect spammers in Weibo. Recently, many scholars have combined deep learning methods to mine contextual features at the tweet level. [PERSON] and [PERSON] [19] designed a deep neural network method that considered context based on the long-term short-term memory (LSTM) architecture, which uses context features extracted from user metadata to detect spambots at the tweet level. Due to the large numbers of social network users, the work of labeling the massive amounts of posted data is complex and error prone which is not applicable in practical applications. In contrast to supervised machine learning, unsupervised machine learning does not rely on labeled data; instead, it uses unlabeled data to build a learning model. [PERSON] and [PERSON] [20] used an aggregate hierarchical clustering method to cluster Twitter users that does not need to wait for occurrences of malicious behavior; it can detect malicious accounts when the account is created. In view of the emerging group spam behavior, [PERSON] et al. [21] proposed a method similar to a clustering algorithm. They generated a corresponding \"digital DNA\" signature by encoding strings with user behavior information and used those to determine the spam similarity between account subgroup sequences. [PERSON] et al. [22] built an unsupervised tool named DeBot by comparing the account time series extracted from the Twitter flow API to find spambots that send tweets synchronously. Unsupervised machine learning methods do not need a large set of labeled data, but their accuracy is usually low as compared with supervised machine learning methods. Semi-supervised learning is a learning method that combines supervised learning with unsupervised learning. [PERSON] et al. [23] proposed a semi-supervised feature selection method based on Laplace score to detect spammers on Twitter. [PERSON] et al. [24] applied the semi-supervised learning to Sybil detection. This method classifies nodes together with information from directed messages and known node labels. [PERSON] et al. [25] fused comprehensive clues explored from multiple views to identify spammers and predicted unlabeled instances iteratively based on a small number of labeled instances in a semi-supervised manner. Previous studies have shown single classifiers are rarely superior to ensemble learning methods on any problem. Ensemble learning achieves better classification performances by training multiple classifiers and, consequently, it usually performs better than single classifiers (also known as base classifiers) [26]. Ensemble learning includes both homogeneous and heterogeneous ensemble learning algorithms. Homogeneous ensemble learning relies on multiple classifier instances of a single type, while heterogeneous ensemble learning uses a variety of different base classifiers to achieve better performance. For example, [PERSON] et al. [17] proposed an ensemble method using three CS-SVMs with different parameters as base classifiers, combined with resampling technology, and achieved good performances for microblog spam detection. [PERSON] et al. [27] developed an ensemble method involving five CNNs and a feature-based model; the metaclassifier used a multilayer neural network and achieved a good performance on Twitter.