Twitter has become one of the main sources of news for many people. As\nreal-world events and emergencies unfold, Twitter is abuzz with hundreds of\nthousands of stories about the events. Some of these stories are harmless,\nwhile others could potentially be life-saving or sources of malicious rumors.\nThus, it is critically important to be able to efficiently track stories that\nspread on Twitter during these events. In this paper, we present a novel\nsemi-automatic tool that enables users to efficiently identify and track\nstories about real-world events on Twitter. We ran a user study with 25\nparticipants, demonstrating that compared to more conventional methods, our\ntool can increase the speed and the accuracy with which users can track stories\nabout real-world events.\n