In recent years, social media has become one of the most popular tools for discovering and following breaking news and ongoing events. However tools and interfaces have lagged behind users' expectations, with current tools making it difficult to discover new events and failing to provide a solution to the problem of information overload. We have developed an interactive interface for visualizing events, backed by a state-of-the-art event detection approach, which is able to detect, track and summarize events in real-time. Our interface provides up-to-the-second information about ongoing events in an easy to understand manner, including category information, temporal distribution, and location information -- all of which was previously unobtainable in real-time.

Query Performance Prediction (QPP) is the estimation of the retrieval success for a query, without explicit knowledge about relevant documents. QPP is especially interesting in the context of Automatic Query Expansion (AQE) based on Pseudo Relevance Feedback (PRF). PRF-based AQE is known to produce unreliable results when the initial set of retrieved documents is poor. Theoretically, a good predictor would allow to selectively apply PRF-based AQE when performance of the initial result set is good enough, thus enhancing the overall robustness of the system. QPP would be of great benefit in the context of microblog retrieval, as AQE was the most widely deployed technique for enhancing retrieval performance at TREC. In this work we study the performance of the state of the art predictors under microblog retrieval conditions as well as introducing our own predictors. Our results show how our proposed predictors outperform the baselines significantly.