  The communication revolution has perpetually reshaped the means through which
people send and receive information. Social media is an important pillar of
this revolution and has brought profound changes to various aspects of our
lives. However, the open environment and popularity of these platforms
inaugurate windows of opportunities for various cyber threats, thus social
networks have become a fertile venue for spammers and other illegitimate users
to execute their malicious activities. These activities include phishing hot
and trendy topics and posting a wide range of contents in many topics. Hence,
it is crucial to continuously introduce new techniques and approaches to detect
and stop this category of users. This paper proposes a novel and effective
approach to detect social spammers. An investigation into several attributes to
measure topic-dependent and topic-independent users' behaviours on Twitter is
carried out. The experiments of this study are undertaken on various machine
learning classifiers. The performance of these classifiers are compared and
their effectiveness is measured via a number of robust evaluation measures.
Further, the proposed approach is benchmarked against state-of-the-art social
spam and anomalous detection techniques. These experiments report the
effectiveness and utility of the proposed approach and embedded modules.
