Classifying text messages for the haiti earthquake

TLDR

In emergencies such as earthquakes, rapid response requires timely information, and social media can self‑organize to provide accurate, early signals that allow affected populations to send tweets or text messages that must be automatically classified and routed to responders. This study aimed to develop a reusable information‑technology infrastructure, EMERSE, to classify and aggregate tweets and text messages about the Haiti disaster relief. EMERSE automatically classifies incoming social‑media messages and aggregates them into categories for non‑governmental organizations, relief workers, Haitians, and their contacts. The resulting system enables these stakeholders to easily access relevant information, improving response efficiency.

Abstract

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), which classifies and aggregates tweets and text messages about the Haiti disaster relief so that non-governmental organizations, relief workers, people in Haiti, and their friends and families can easily access them.