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Research Spending & Results

Award Detail

Awardee:KANSAS STATE UNIVERSITY
Doing Business As Name:Kansas State University
PD/PI:
  • Cornelia Caragea
  • (814) 308-4974
  • ccaragea@ksu.edu
Award Date:12/01/2017
Estimated Total Award Amount: $ 229,707
Funds Obligated to Date: $ 229,707
  • FY 2015=$229,707
Start Date:10/26/2017
End Date:08/31/2018
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.070
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:CHS: Small: Collaborative Research: Automating Relevance and Trust Detection in Social Media Data for Emergency Response
Federal Award ID Number:1814271
DUNS ID:929773554
Parent DUNS ID:041146432
Program:Cyber-Human Systems (CHS)
Program Officer:
  • William Bainbridge
  • (703) 292-8930
  • wbainbri@nsf.gov

Awardee Location

Street:2 FAIRCHILD HALL
City:Manhattan
State:KS
ZIP:66506-1100
County:Manhattan
Country:US
Awardee Cong. District:01

Primary Place of Performance

Organization Name:Kansas State University
Street:2 FAIRCHILD HALL
City:Manhattan
State:KS
ZIP:66506-1100
County:Manhattan
Country:US
Cong. District:01

Abstract at Time of Award

The goal of this project is to develop means to improve information quality and use in emergency response, increasing the value of using messaging and microblogged data from crowds of non-professional participants during disasters. Despite the evidence of strong value to those experiencing the disaster and those seeking information concerning the disaster, there has been very little effort in detecting the relevance and veracity of messages in social media streams. The problem of data verification is one of the largest problems confronting emergency-response organizations contemplating using social media data. This research directly addresses this known problem by methods to measure relevant and verifiable information. The results of this research will have a direct pipeline to organizations involved in emergency response. Therefore the research has the potential to help organizations, which respond to emergencies, make use of large amounts of citizen-produced data, which in turn may improve the speed, quality, and efficiency of emergency response leading to better support to those who need them, and more lives saved. This research will contribute to the field of Emergency and Disaster Studies by mapping the key decisions made during an emergency response, the information needs, type, form and flow during those decision points, and most importantly, assessing data quality and verifiable standards for each. It will also investigate relevant and verifiable identifiers (or features), provide weights, incorporate these into an analytical framework, and use the results of the analysis as input to scalable computational models. The work will design algorithms that can estimate the relevance and veracity of messages in a high-volume streaming text comprised of short messages. Given the diverse backgrounds of the team, it will contribute to the use and development of socio-technical systems theory to analyze the integration of technical and social systems. The output of the models will match the organizational needs of responding organizations.

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