Emergency-relief coordination on social media: Automatically matching resource requests and offers

Authors

  • Hemant Purohit Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University
  • Carlos Castillo Qatar Computing Research Institute (QCRI)
  • Fernando Diaz Microsoft Research NYC
  • Amit Sheth Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University
  • Patrick Meier Qatar Computing Research Institute (QCRI)

DOI:

https://doi.org/10.5210/fm.v19i1.4848

Keywords:

emergency response, crisis response, disaster response, relief coordination, crisis coordination, donation coordination, request-offer identification, request-offer matching

Abstract

Disaster affected communities are increasingly turning to social media for communication and coordination. This includes reports on needs (demands) and offers (supplies) of resources required during emergency situations. Identifying and matching such requests with potential responders can substantially accelerate emergency relief efforts. Current work of disaster management agencies is labor intensive, and there is substantial interest in automated tools.

We present machine–learning methods to automatically identify and match needs and offers communicated via social media for items and services such as shelter, money, clothing, etc. For instance, a message such as “we are coordinating a clothing/food drive for families affected by Hurricane Sandy. If you would like to donate, DM us” can be matched with a message such as “I got a bunch of clothes I’d like to donate to hurricane sandy victims. Anyone know where/how I can do that?” Compared to traditional search, our results can significantly improve the matchmaking efforts of disaster response agencies.


Author Biographies

Hemant Purohit, Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University

Dept. of Computer Science and Engineering

 

Researcher

Carlos Castillo, Qatar Computing Research Institute (QCRI)

Social Computing Group

 

Senior Scientist

Fernando Diaz, Microsoft Research NYC

Web Search group

 

Researcher

Amit Sheth, Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University

Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis)

 

Director and LexisNexis Ohio Eminent Scholar

 

Patrick Meier, Qatar Computing Research Institute (QCRI)

Social Innovation group

 

Director

Downloads

Published

2013-12-28

How to Cite

Purohit, H., Castillo, C., Diaz, F., Sheth, A., & Meier, P. (2013). Emergency-relief coordination on social media: Automatically matching resource requests and offers. First Monday, 19(1). https://doi.org/10.5210/fm.v19i1.4848