Twitter has been heavily used as an important channel for communicating and discussing about events in real-time. In such major events, many uninformative tweets are also published rapidly by many users, making it hard to follow the events. In this paper, we address this problem by investigating machine learning methods for automatically identifying informative tweets among those that are relevant to a target event. We examine both traditional approaches with a rich set of handcrafted features and state of the art approaches with automatically learned features. We further propose a hybrid model that leverages both the handcrafted features and the automatically learned ones. Our experiments on several large datasets of real-world events show that the latter approaches significantly outperform the former and our proposed model performs the best, suggesting highly effective mechanisms for tracking mass events.

Web archiving is the process of gathering data from the Web, storing it and ensuring the data is preserved in an archive for future explorations. Despite the increasing number of web archives, the absence of meaningful exploration methods remains a major hurdle in the way of turning them into a useful information source. With the creation of profiles describing metadata information about the archived documents it is possible to offer a more exploitable environment that goes beyond the simple keyword-based search. By exploring the expressive power of SPARQL language and providing a user friendly web-based search interface, users can run sophisticated queries searching for documents that meet their information needs.