Classifying microblogs for disasters

Abstract

Monitoring social media in critical disaster situations can potentially assist emergency and media personnel to deal with events as they unfold, and focus their resources where they are most needed. We address the issue of filtering massive amounts of Twitter data to identify high-value messages related to disasters, and to further classify disaster-related messages into those pertaining to particular disaster types, such as earthquake, flooding, fire, or storm. Unlike post-hoc analysis that most previous studies have done, we focus on building a classification model on past incidents to detect tweets about current incidents. Our experimental results demonstrate the feasibility of using classification methods to identify disaster-related tweets. We analyse the effect of different features in classifying tweets and show that using generic features rather than incident-specific ones leads to better generalisation on the effectiveness of classifying unseen incidents.