Sub-event discovery and retrieval during natural hazards on social media data

Social media sites contain a considerable amount of data for natural calamities events, such as earthquakes, snowstorms, mud-rock flows. With the increasing amount of social media data, an important task is to discover and retrieve sub-events over time. Especially in emergency situations, rescue and relief activities can be enhanced by identifying and retrieving sub-events of a natural hazard event. However, the existing event detection techniques in news-related reports cannot effectively work for social media data due to the unstructured of social network data. In this paper, we propose a new natural hazard sub-events discovery model SED (Sub-Events Discovery), which adopts multifarious features to detect sub-events. Moreover, in order to retrieve the sub-events over a specific event, we introduce a novel SER (Sub-Event Retrieval) algorithm from time-stamped social media data. Our novel approach SER makes use of automatically obtained messages from external search engines in the entire process. For purpose of determining the periodical convergence time for natural hazard event, our method provides online sub-events retrieval and sub-events discovery to meet the further needs. Next the improved estimation standards with timestamp are utilized in our experiments to verify the effectiveness and efficiency of SED model and SER algorithm.