Classifying text messages for the haiti earthquake

Cornelia Caragea, Nathan McNeese, Anuj Jaiswal, Greg Traylor, Hyun Woo Kim, Prasenjit Mitra, Dinghao Wu, Andrea H. Tapia, Lee Giles, Bernard Jansen, John Yen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

90 Citations (Scopus)

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.

Original languageEnglish
Title of host publication8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011
PublisherInformation Systems for Crisis Response and Management, ISCRAM
ISBN (Print)9789724922478
Publication statusPublished - 2011
Externally publishedYes
Event8th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2011 - Lisbon
Duration: 8 May 201111 May 2011

Other

Other8th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2011
CityLisbon
Period8/5/1111/5/11

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Keywords

  • Abstract features
  • Machine learning
  • Text message classification

ASJC Scopus subject areas

  • Information Systems

Cite this

Caragea, C., McNeese, N., Jaiswal, A., Traylor, G., Kim, H. W., Mitra, P., Wu, D., Tapia, A. H., Giles, L., Jansen, B., & Yen, J. (2011). Classifying text messages for the haiti earthquake. In 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011 Information Systems for Crisis Response and Management, ISCRAM.