Abstract
Decision makers use information from a range of terrestrial and online sources to help underpin the processes through which they develop policies and react to events as they unfold. One such source of online information is social media. Twitter, as a form of social media, is a popular micro-blogging Web application serving hundreds of millions of users. User-generated content can be exploited as a rich source of information for identifying 'real-world' disruptive events. In this paper, we present an in-depth comparison of three types of features that could be useful for identifying disruptive events: temporal, spatial and textual. We make several interesting observations: first, disruptive events are identifiable regardless of the "influence of the user" discussing them, and over a variety of topics. Second, temporal features are the best event identifiers and hence should not be disregarded or ignored. Third, a combination of optimum textual features with temporal and spatial features achieves best performance in the event detection task. We believe that these findings provide new insights for gathering information around real-world events as well as a useful resource for improving situational awareness and decision support.