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 J. Jansen, John Yen

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

81 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 (US)
Title of host publication8th International Conference on Information Systems for Crisis Response and Management
Subtitle of host publicationFrom Early-Warning Systems to Preparedness and Training, ISCRAM 2011
PublisherInformation Systems for Crisis Response and Management, ISCRAM
ISBN (Print)9789724922478
StatePublished - Jan 1 2011
Event8th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2011 - Lisbon, Portugal
Duration: May 8 2011May 11 2011

Publication series

Name8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011

Other

Other8th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2011
CountryPortugal
CityLisbon
Period5/8/115/11/11

Fingerprint

Earthquakes
Personnel
Disasters
Information technology
Communication

All Science Journal Classification (ASJC) codes

  • Information Systems

Cite this

Caragea, C., McNeese, N., Jaiswal, A., Traylor, G., Kim, H. W., Mitra, P., ... 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 (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.
Caragea, Cornelia ; McNeese, Nathan ; Jaiswal, Anuj ; Traylor, Greg ; Kim, Hyun Woo ; Mitra, Prasenjit ; Wu, Dinghao ; Tapia, Andrea H. ; Giles, Lee ; Jansen, Bernard J. ; Yen, John. / Classifying text messages for the haiti earthquake. 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, 2011. (8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011).
@inproceedings{dd46b3f0e3434269a829ac4a42e1bd65,
title = "Classifying text messages for the haiti earthquake",
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.",
author = "Cornelia Caragea and Nathan McNeese and Anuj Jaiswal and Greg Traylor and Kim, {Hyun Woo} and Prasenjit Mitra and Dinghao Wu and Tapia, {Andrea H.} and Lee Giles and Jansen, {Bernard J.} and John Yen",
year = "2011",
month = "1",
day = "1",
language = "English (US)",
isbn = "9789724922478",
series = "8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011",
publisher = "Information Systems for Crisis Response and Management, ISCRAM",
booktitle = "8th International Conference on Information Systems for Crisis Response and Management",

}

Caragea, C, McNeese, N, Jaiswal, A, Traylor, G, Kim, HW, Mitra, P, Wu, D, Tapia, AH, Giles, L, Jansen, BJ & 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. 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, 8th International Conference on Information Systems for Crisis Response and Management, ISCRAM 2011, Lisbon, Portugal, 5/8/11.

Classifying text messages for the haiti earthquake. / Caragea, Cornelia; McNeese, Nathan; Jaiswal, Anuj; Traylor, Greg; Kim, Hyun Woo; Mitra, Prasenjit; Wu, Dinghao; Tapia, Andrea H.; Giles, Lee; Jansen, Bernard J.; Yen, John.

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, 2011. (8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011).

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

TY - GEN

T1 - Classifying text messages for the haiti earthquake

AU - Caragea, Cornelia

AU - McNeese, Nathan

AU - Jaiswal, Anuj

AU - Traylor, Greg

AU - Kim, Hyun Woo

AU - Mitra, Prasenjit

AU - Wu, Dinghao

AU - Tapia, Andrea H.

AU - Giles, Lee

AU - Jansen, Bernard J.

AU - Yen, John

PY - 2011/1/1

Y1 - 2011/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84905666187&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84905666187&partnerID=8YFLogxK

M3 - Conference contribution

SN - 9789724922478

T3 - 8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011

BT - 8th International Conference on Information Systems for Crisis Response and Management

PB - Information Systems for Crisis Response and Management, ISCRAM

ER -

Caragea C, McNeese N, Jaiswal A, Traylor G, Kim HW, Mitra P et al. 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. 2011. (8th International Conference on Information Systems for Crisis Response and Management: From Early-Warning Systems to Preparedness and Training, ISCRAM 2011).