## 1 Introduction  
Currently, many internet users can impart information and work together inside online social networks (OSNs). However, Twitter is viewed as the most well-known informal community which offers free blogging services for clients to publish their news and thoughts inside 280 characters. Clients can follow others through various platforms [1]. Consistently, a huge number of Twitter clients share their status and news about their disclosures [2]. Moreover, the Twitter platform additionally attracts criminal records (spammers) that can tweet spam substances, which may incorporate destructive URLs. This could divert clients to malevolent or phishing sites for bringing in cash misguidedly [3, 4] by assaulting the client's profile. As Twitter set caps for the length of the characters of tweets, this makes spammer swindle clients by putting cheat content or malicious URL to divert them for the outside site [5]. In an investigation studying the correlation between both email and social spam, the click-through rate of Twitter spam was found to reach 0.13%, in spite of the fact that email spam arrives at 0.0003-0.0006% [6]. Moreover, social spam is viewed as increasingly perilous and cheats a lot of clients [7].  
To tackle this problem, many researchers are focusing on detecting spammers by discovering the statistical features of spammers on both messaging and account levels. These messaging detection approaches focus on checking tweet content to find keyword patterns, hashtags, and URLs. These approaches are shown to be effective, but real-time detection is needed to solve the huge number of messages which are posted per hour. The account level approaches focus on extracting statistics and info about the behavior of each account to classify whether they are spam accounts or legitimate users. However, an experimental study was conducted to examine whether the statistical features changed over time. The experimental results proved that the statistical features are changed over time.  
Most of the researchers are focusing on collecting these features and trying to experience spammers priorities, ignoring that these features drift over time. However, spammers will try to tackle all these features. In this paper, an effective technique has been proposed to tackle the aforementioned limitations. Our proposed technique focuses on the content of each tweet in addition to the statistical features. Moreover, it has an auto-learning capability to find the features which make it able to classify each tweet as spam or not with high accuracy in a reasonable time.  
Accordingly, these challenges inspire us to investigate this problem to contribute to spam detection approaches. To cope with this problem, we maintain a framework that contains three stages to detect spammers:  
* Fast filter mode classifier to determine whether each input tweet is spam or not.
* Every filtered spam tweet is paraphrased to generate a new spam sentence with different definition with the same meaning.
* Ensemble deep learning methods are collected in addition to the statistical features to decide the output of the classifier.  
The rest of this manuscript is organized as follows. Section 2 briefly discusses the literature review on Twitter spam detection. Section 3 clarifies the problem statement of spam drift in detail. Section 4 explains our proposed detection framework. Section 5 discusses our experiments and results. Finally, conclusions are represented in Section 6.