Micro-blogging services such as Twitter can develop into valuable sources of up-to-date information provided the spam problem is overcome. Thus, separating the most relevant users from the spammers is a highly pertinent question for which graph centrality methods can provide an answer. In this paper we examine the vulnerability of five different algorithms to linking malpractice in Twitter and propose a first step towards "desensitizing" them against such abusive behavior.