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Twitter is one of the most well known micro-blogging administrations, which is commonly used to share news and updates through short messages confined to 280 characters. In any case, its open nature and huge client base are every now and again misused via robotized spammers, content polluters, and other not well expected clients to carry out different cyber violations, for example, cyber bullying, trolling, rumor dissemination, and stalking. Likewise, various methodologies have been proposed by specialists to address these issues. Nonetheless, the majority of these methodologies depend on client portrayal and totally dismissing shared communications. In this examination, we present a hybrid methodology for recognizing mechanized spammers by amalgamating network based features with other feature classifications, to be specific metadata-, content-, and association based features. The curiosity of the proposed methodology lies in the portrayal of clients dependent on their communications with their supporters given that a client can dodge features that are identified with his/her very own exercises, yet sidestepping those dependent on the devotees is troublesome. Nineteen distinct features, including six recently characterized features and two re-imagined features, are distinguished for learning three classifiers, in particular, irregular woods, choice tree, Bayesian system, and example pre-handling on a genuine dataset that involves generous clients and spammers. The separation intensity of various feature classifications is additionally broke down, and cooperation and network based features are resolved to be the best for spam identification, though metadata-based features are demonstrated to be the least compelling.