Information streams provide today a prevalent way of publishing and consuming content on the Web, especially due to the great success of social networks. Top-k queries over the streams of interest allow limiting results to the most relevant content, while continuous processing of such queries is the most effective approach in large scale systems. However, current systems fail in combining continuous top-k processing with rich scoring models including social network criteria. We present here the SANTA algorithm, able to handle scoring functions including content similarity, but also social network criteria and events in a continuous processing of top-k queries. We propose a variant (SANTA+) that accelerates the processing of interaction events in social networks. We compare SANTA/SANTA+ with an extension of a state-of-the-art algorithm and report a rich experimental study of our approach.