Classifying Text Messages for the Haiti Earthquake

In case of emergencies (e.g., earthquakes, flooding), rapid responses are needed in order to address victims’ requests for help. Social media used around crises involves self-organizing behavior that can produce accurate results, often in advance of official communications. This allows affected population to send tweets or text messages, and hence, make them heard. The ability to classify tweets and text messages automatically, together with the ability to deliver the relevant information to the appropriate personnel are essential for enabling the personnel to timely and efficiently work to address the most urgent needs, and to understand the emergency situation better. In this study, we developed a reusable information technology infrastructure, called Enhanced Messaging for the Emergency Response Sector (EMERSE). The components of EMERSE are: (i) an iPhone application; (ii) a Twitter crawler component; (iii) machine translation; and (iv) automatic message classification. While each component is important in itself and deserves a detailed analysis, in this paper we focused on the automatic classification component, which classifies and aggregates tweets and text messages about the Haiti disaster relief so that they can be easily accessed by non-governmental organizations, relief workers, people in Haiti, and their friends and families.