Feature extraction and analysis for identifying disruptive events from social media

Disruptive event identification is a concept that is crucial to ensuring public safety regarding large-scale events. Recent work on detecting events from social media shows that although these platforms are used for social purposes, they have been emerging as important source of information. 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 - events that threaten social safety and security, or could cause disruption to social order. In this paper, we present an in-depth comparison of two types of feature that could be useful for identifying disruptive events: temporal and textual features. On the basis of these features, we investigate the dynamics of event/topic identification over time. 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 play a central role in event detection and hence should not be disregarded or ignored. Third, textual features can be used to improve the overall performance of the event detection. We believe that these findings provide new insights for gathering information around real-world events, in particular for detecting disruptive events.