Web 2.0 technologies have transformed the Web from a publishing only environment into a vibrant information place where yesterday’s end users become nowadays content generators themselves. The vast amounts of user generated content available in various social media (Facebook, Twitter, blogs, discussion forums) in conjunction with traditional information producers (e.g., newspapers, television, radio) poses new challenges in achieving an effective, near real-time information awareness. In this talk we present recent results for supporting users to state their interests as continuous textual queries which will be matched on the fly against incoming information items originating from different sources. In particular, we will present in three complementary problems related to effective and efficient continuous filtering systems, namely (a) scalable indices for textual subscriptions in a Pub/Sub system (b) top-k continuous query evaluation with scoring functions (c) multi-query optimization for continuous textual mashups. The advocated solutions aim to meet the requirements for processing very large volumes of varying quality information published at different rates.