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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: $ 400,000
Funds Obligated to Date: $ 400,000
  • FY 2017=$400,000
Start Date:09/20/2017
End Date:12/31/2021
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:BIGDATA: IA: Collaborative Research: Domain Adaptation Approaches for Classifying Crisis Related Data on Social Media
Federal Award ID Number:1802284
DUNS ID:929773554
Parent DUNS ID:041146432
Program:Big Data Science &Engineering
Program Officer:
  • Aidong Zhang
  • (703) 292-8930
  • azhang@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:1601 Vattier Street
City:Manhattan
State:KS
ZIP:66506-1100
County:Manhattan
Country:US
Cong. District:01

Abstract at Time of Award

The project investigates the use of big-data analysis techniques for classifying crisis-related data in social media with respect to situational awareness categories, such as caution, advice, fatality, injury, and support, with the goal of helping emergency response teams identify useful information. A major challenge is the scale of the data, where millions of short messages are continuously posted during a disaster, and need to be analyzed. The use of current technologies based on automated machine learning is limited due to the lack of labeled data for an emergent target disaster, and the fact that every event is unique in terms of geography, culture, infrastructure, technology, and the people involved. To tackle the above challenges, domain adaptation techniques that make use of existing labeled data from prior disasters and unlabeled data from a current disaster are designed. The resulting models are continuously updated and improved based on feedback from crowdsourcing volunteers. The research will provide real, usable solutions to emergency response organizations and will enable these organizations to improve the speed, quality and efficiency of their response. The research provides novel solutions based on domain adaptation and deep neural networks to tackle the unique challenges in applying machine learning for crisis-related data analysis, specifically the volume and velocity challenges of big crisis data. Domain adaptation approaches enable the transfer of information from prior source disasters to an emergenet target disaster. Deep learning approaches make it possible to employ large amounts of labeled source data and unlabeled target data, and to incrementally update the models as more labeled target data becomes available. Large-scale analysis across combinations of source and target crises will help identify patterns of transferable situational awareness knowledge. The resulting technical and social solutions will be blended together for use in data management and emergency response.

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