Due to the massive success of social media, online user-generated content has increased exponentially in the last years. Twitter, as a microblogging platform, allows users to share information about their opinions or activities by means of short posts called tweets. However, opinion spammers see social networks like Twitter as an opportunity to propagate their ideas, promoting or discrediting some target product or service, without showing their true intentions. In this study, we focused on detecting suspicious users who posted dubious claims about cancer treatment and prevention on Twitter. We addressed the task with a supervised learn- ing approach, a binary classi cation problem in which we had to predict whether users were suspicious or genuine.