While social media presence is increasingly important for businesses, growing a social media account and improving its reputation by gathering followers are time-consuming tasks, especially for professionals and small businesses lacking the necessary skills and resources. With the broader goal of providing automatic tool support for social media account automation, in this paper we consider the problem of recommending a Twitter account manager a top-K list of Twitter users that, if approached—e.g., followed, mentioned, or otherwise targeted on social media—are likely to follow the account and interact with it, this way improving its reputation. We propose a recommendation system tackling this problem that leverages features ranging from basic social media attributes to specialized, domain-relevant user profile attributes predicted from data using machine learning techniques, and we report on a preliminary analysis of its performance in gathering new followers in a Twitter scenario where the account manager follows recommended users to trigger their follow-back.