Social media systems have increasingly become digital information marketplaces, where users produce, consume and share information and ideas, often of public interest. In this context, social media users are their own curators of information — however, they can only select their information sources, who they follow, but cannot choose the information they are exposed to, which content they receive. A natural question is thus to assess how efficient are users at selecting their information sources. In this work, we model social media users as information processing systems whose goal is acquiring a set of (unique) pieces of information. We then define a computational framework, based on minimal set covers, that allows us both to evaluate every user's performance as information curators within the system. Our framework is general and applicable to any social media system where every user follows others within the system to receive the information they produce. We leverage our framework to investigate the efficiency of Twitter users at acquiring information. We find that user's efficiency typically decreases with respect to the number of people she follows. A more efficient user tends to be less overloaded and, as a consequence, any particular piece of information lives longer in the top of her timeline, thus facilitating her to actually read the information. Finally, while most unique information a user receives could have been acquired through a few users, less popular information requires following many different users.