Social media has become the ideal platform for promotional activities of organizations. However, due to the volatility of social media, the wrong message posted at the wrong time can result in significant damage to hard-built brand image. This calls for a mechanism to gauge the reactions a post will evoke from a given social community. The community can vary from customers of a particular brand to brand loyalists interacting through its social pages (for example, on Facebook). In this paper, we focus on learning the community's reaction from past posts and providing a predictive model for gauging the reaction of the community before the post is published. This helps the marketer take better-informed decisions. Short-text posts in social media leads to a sparse feature space, we propose additional meta-features that improve reaction modeling. Given the feature representation, we discuss the possibility of casting the underlying problem under different paradigms - classification, regression and learning to rank. We study the performances of each of these paradigms on real data from Facebook. We will discuss the challenges involved, and ways to mitigate them, in addition to our observations, results and insights.