The observation of social media provides an important complementing source of information about an unfolding event such as a crisis situation. For this purpose we have developed and demonstrate Sover!, a system to monitor real-time dynamic events via Twitter targeting the needs of aid organizations. At its core it builds upon an effective adaptive crawler, which combines two social media streams in a Bayesian inference framework and after each time-window updates the probabilities of whether given keywords are relevant for an event. Sover! also exposes the crawling functionality so a user can actively influence the evolving selection of keywords. The crawling activity feeds a rich dashboard, which enables the user to get a better understanding of a crisis situation as it unfolds in real-time.

Stochastic Gradient Descent (SGD) is a simple yet very efficient online learning algorithm for optimizing convex (and often non-convex) functions and one of the most popular stochastic optimization methods in machine learning today. One drawback of SGD is that it is sensitive to the learning rate hyper-parameter. The Adaptive Sub-gradient Descent, AdaGrad, dynamically incorporates knowledge of the geometry of the data observed in earlier iterations to calculate a different learning rate for every feature. In this work, we implement a distributed version of AdaGrad for large-scale machine learning tasks using Apache Spark. Apache Spark is a fast cluster computing engine that provides similar scalability and fault tolerance properties to MapReduce, but in contrast to Hadoop's two-stage disk-based MapReduce paradigm, Spark's multi-stage in-memory primitives allow user programs to load data into a cluster's memory and query it repeatedly, which makes it ideal for building scalable machine learning applications. We empirically evaluate our implementation on large-scale real-world problems in the machine learning canonical tasks of classification and regression. Comparing our implementation of AdaGrad with the SGD scheduler currently available in Spark's Machine Learning Library (MLlib), we experimentally show that AdaGrad saves time by avoiding manually setting a learning-rate hyperparameter, converges fast and can even achieve better generalization errors.

Trending topics in microblogs such as Twitter are valuable resources to understand social aspects of real-world events.To enable deep analyses of such trends, semantic annotation is an effective approach; yet the problem of annotating microblog trending topics is largely unexplored by the research community.In this work, we tackle the problem of mapping trending Twitter topics to entities from Wikipedia.We propose a novel model that complements traditional text-based approaches by rewarding entities that exhibit a high temporal correlation with topics during their burst time period.By exploiting temporal information from the Wikipedia edit history and page view logs, we have improved the annotation performance by 17-28%, as compared to the competitive baselines.