  Nowadays, with the rise of Internet access and mobile devices around the
globe, more people are using social networks for collaboration and receiving
real-time information. Twitter, the microblogging that is becoming a critical
source of communication and news propagation, has grabbed the attention of
spammers to distract users. So far, researchers have introduced various defense
techniques to detect spams and combat spammer activities on Twitter. To
overcome this problem, in recent years, many novel techniques have been offered
by researchers, which have greatly enhanced the spam detection performance.
Therefore, it raises a motivation to conduct a systematic review about
different approaches of spam detection on Twitter. This review focuses on
comparing the existing research techniques on Twitter spam detection
systematically. Literature review analysis reveals that most of the existing
methods rely on Machine Learning-based algorithms. Among these Machine Learning
algorithms, the major differences are related to various feature selection
methods. Hence, we propose a taxonomy based on different feature selection
methods and analyses, namely content analysis, user analysis, tweet analysis,
network analysis, and hybrid analysis. Then, we present numerical analyses and
comparative studies on current approaches, coming up with open challenges that
help researchers develop solutions in this topic.
