A Semi-Automatic Method for Efficient Detection of Stories on Social Media

Abstract

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